Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states 'Create a new project' which implies a write/mutation operation, but doesn't disclose behavioral traits like required permissions, rate limits, whether creation is idempotent, or what happens on failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('Create a new project') and purpose. There's zero waste—every word contributes to understanding the tool's function. It's appropriately sized for a creation tool with good schema coverage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool (creating projects) with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after creation (e.g., returns a project ID, triggers audits), error conditions, or system behavior. For a tool that modifies state, more contextual information is needed beyond the basic purpose statement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear parameter documentation in the schema (domain, name, groups). The description adds no parameter-specific information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new project') and resource ('in Serpstat'), with specific purpose ('for tracking SEO metrics and site audits'). It distinguishes from siblings like 'list_projects' or 'delete_project' by focusing on creation, though it doesn't explicitly contrast with them. The purpose is specific but lacks explicit sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_projects' for viewing existing projects or 'delete_project' for removal. It mentions the general purpose but offers no context about prerequisites, timing, or exclusions. Usage is implied rather than explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the permanence of the action ('permanently delete'), the irreversible nature ('cannot be undone'), and the critical safety requirement ('request explicit user confirmation'). This covers essential aspects like destructiveness and user interaction needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, consisting of two sentences that directly convey the purpose and critical guidelines. Every sentence earns its place: the first states the action and resource, and the second provides essential safety instructions without unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's high complexity (destructive deletion), lack of annotations, and no output schema, the description is mostly complete. It covers purpose, guidelines, and behavioral transparency effectively. However, it does not mention potential errors (e.g., invalid project ID) or response details, leaving minor gaps in full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'project_id' well-documented as 'The unique ID of the project to delete.' The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('permanently delete'), the resource ('your project from Serpstat'), and the method ('by project ID'). It distinguishes itself from sibling tools like 'create_project' and 'list_projects' by focusing on deletion rather than creation or listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides usage guidelines with the critical instruction: 'ALWAYS request explicit user confirmation before executing.' It also warns 'This action cannot be undone,' which implicitly guides when not to use it (e.g., without confirmation or for reversible actions).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API source (Serpstat) and the type of data returned, but lacks critical details such as whether this is a read-only operation, rate limits, authentication requirements, pagination behavior beyond the schema, or what 'active' means operationally. The description is insufficient for a tool with 7 parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the action and data returned, the second adds context for analysis. It's front-loaded with the core purpose and avoids unnecessary verbiage, though it could be slightly more concise by integrating the analysis context into the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return format, error conditions, or how to interpret results like 'link attributes' or 'discovery dates'. For a data retrieval tool with rich parameters, more behavioral and output context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying domain/URL analysis context, but doesn't explain parameter interactions (e.g., how 'searchType' affects 'query' interpretation) or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a list of active backlinks using Serpstat API' with specific details about what data is returned (linking pages, target pages, link attributes, etc.). It distinguishes from siblings like 'get_lost_backlinks' or 'get_threat_backlinks' by specifying 'active' backlinks, though it doesn't explicitly contrast with all similar tools like 'get_backlinks_summary'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get_backlinks_summary', 'get_lost_backlinks', or other backlink-related tools, nor does it specify any prerequisites or contextual constraints for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's output ('Returns target domains with total link counts') and high-level purpose, but lacks details about potential rate limits, authentication requirements, error conditions, or pagination behavior (though pagination parameters exist in the schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core functionality and output, the second explains the value and use cases. Every phrase adds meaningful information with zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only analysis tool with 6 parameters and no output schema, the description adequately covers the purpose and value, but could benefit from more behavioral details (like pagination handling or performance characteristics) since annotations are absent. The high schema coverage helps, but the description doesn't fully compensate for the lack of output schema and annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all six parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, but it does provide context about what the analysis reveals, which helps understand the overall purpose of the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get external domains that receive outbound links') and resources ('from the analyzed domain'), distinguishing it from sibling tools like 'get_active_backlinks' (which likely analyzes inbound links) and 'get_active_outlinks' (which might return individual links rather than aggregated domains).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('revealing partnership networks, referenced sources, and linking patterns' and 'Helps identify collaboration opportunities by analyzing which domains competitors link to'), but it doesn't explicitly mention when not to use it or name specific alternative tools among the many siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (external links with attributes) and its purpose, but doesn't mention rate limits, authentication requirements, pagination behavior beyond what's in the schema, or whether this is a read-only operation. The description adds some value but lacks comprehensive behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences: the first states the purpose and return values, the second provides use cases. It's front-loaded with essential information and avoids unnecessary repetition. Every sentence adds value, though it could be slightly more structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 parameters, no output schema, no annotations), the description provides adequate purpose and usage context but lacks details about return format structure, error conditions, or behavioral constraints. It's complete enough for basic understanding but leaves gaps for a tool with this parameter count and no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, but it does provide context about what 'active outbound links' means. This meets the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get active outbound links') and resources ('from a domain or URL'), and distinguishes it from siblings like 'get_active_backlinks' by focusing on outbound rather than inbound links. The description explicitly mentions what it returns and its use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('Useful for analyzing linking strategies, finding partnership opportunities, and auditing outbound link profiles'), but doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools. It implies usage for outbound link analysis versus inbound (backlinks).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'Returns anchor texts used in backlinks, with metrics including referring domains, total backlinks, and nofollow counts,' which describes output content but lacks critical behavioral details. It doesn't mention rate limits, authentication requirements, pagination behavior (implied by 'page' and 'size' parameters but not explained), error handling, or whether it's a read-only operation. For a tool with 8 parameters and no annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured in two sentences: the first states the purpose and technology, the second details the return metrics. It's front-loaded with the core function and avoids unnecessary fluff. However, it could be slightly more efficient by integrating the technology mention ('using Serpstat API') into the first clause without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and output metrics but misses crucial context: no guidance on usage vs. siblings, limited behavioral transparency, and no explanation of how parameters like 'count' or 'sort' affect results. For a data retrieval tool in a crowded namespace of backlink-related tools, this leaves significant gaps for an agent to operate effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal parameter semantics beyond the input schema. It mentions 'domain or URL analysis' which relates to the 'query' and 'searchType' parameters, and 'anchor text' which relates to the 'anchor' parameter, but doesn't explain how these interact or provide usage examples. With 100% schema description coverage, the schema already documents all parameters thoroughly, so the baseline is 3. The description doesn't compensate with additional insights like parameter interdependencies or practical tips.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get anchor text analysis for backlinks using Serpstat API.' It specifies the action ('Get'), resource ('anchor text analysis for backlinks'), and technology ('Serpstat API'). However, it doesn't explicitly differentiate from sibling tools like 'get_top10_anchors' or 'get_active_backlinks', which appear related to backlink analysis, leaving some ambiguity about when to choose this specific tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'domain or URL analysis' but doesn't specify scenarios, prerequisites, or comparisons with sibling tools (e.g., 'get_top10_anchors' for top anchors or 'get_active_backlinks' for active backlinks). This lack of context makes it difficult for an agent to select this tool appropriately among many backlink-related options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns ('intersection data showing which donors link to multiple domains... including link metrics, anchor texts, and domain authority scores'), which is helpful. However, it lacks details on rate limits, authentication needs, error conditions, or pagination behavior beyond what the schema implies.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in three sentences: the first states the purpose, the second explains the use case, and the third details the return data. Each sentence adds value with zero waste, making it front-loaded and appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage context, and return data, though it could benefit from more behavioral details like pagination handling or error scenarios. The absence of an output schema makes the return description valuable, but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds no specific parameter semantics beyond implying that 'query' is the main domain and 'intersect' are competitors, which is already clear from the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get backlinks from domains that link to multiple analyzed sites simultaneously.' It specifies the verb ('Get backlinks'), resource ('domains'), and scope ('intersection analysis'), distinguishing it from sibling tools like 'get_active_backlinks' or 'get_referring_domains' that likely handle different backlink queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'useful for competitive backlink analysis and identifying potential link sources.' It implies usage for comparing a target domain with competitors, but does not explicitly state when not to use it or name specific alternatives among the many sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API source (Serpstat) and return data types, but lacks critical details such as rate limits, authentication requirements, pagination, error handling, or whether this is a read-only operation. For a data-fetching tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: one stating the action and API, and another listing return data and scope. It's front-loaded with the core purpose and avoids unnecessary details, though it could be slightly more polished (e.g., 'Returns' could be integrated into the first sentence).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of backlink analysis, no annotations, and no output schema, the description is incomplete. It doesn't explain the format or structure of the returned 'comprehensive summary' (e.g., JSON fields, metrics like DA/PA), nor does it cover behavioral aspects like data freshness or limitations. For a tool with rich sibling alternatives, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds marginal value by clarifying that 'query' is for 'domain or subdomain to analyze' and implying 'searchType' affects scope, but doesn't provide additional syntax or format details beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get comprehensive backlinks summary using Serpstat API' with specific resources (referring domains, backlinks count, etc.) and scope (domain or subdomain). It distinguishes from siblings like 'get_active_backlinks' or 'get_referring_domains' by emphasizing a 'comprehensive summary' rather than specific subsets, though it doesn't explicitly name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like 'get_active_backlinks' or 'get_referring_domains' is provided. The description implies usage for a broad summary, but lacks context on prerequisites, timing, or exclusions, leaving the agent to infer based on sibling tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the tool 'Returns ranked domains' and includes 'filtering and sorting options,' which gives some behavioral insight. However, it lacks details on rate limits, authentication needs, pagination behavior (beyond parameters), or error handling, leaving gaps for a tool with 7 parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by prerequisites and return context. It uses three sentences efficiently, with no redundant information. However, it could be slightly more structured by separating usage notes from output details, but overall it's concise and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 parameters, nested objects, no output schema, and no annotations), the description is moderately complete. It covers purpose, prerequisites, and high-level output, but lacks details on behavioral traits like rate limits or error handling. Without annotations or output schema, it should provide more context on what the ranked domains output looks like, but it's adequate for basic understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, mentioning 'SEO metrics (traffic, visibility, keywords, backlinks, SDR)' and 'filtering and sorting options,' but does not explain parameter interactions or provide additional context. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get top-performing domains in a specific market category with SEO metrics (traffic, visibility, keywords, backlinks, SDR).' It specifies the verb ('Get'), resource ('top-performing domains'), and scope ('in a specific market category'), distinguishing it from sibling tools like 'get_domain_competitors' or 'get_domains_info' by focusing on category-based ranking with SEO metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Requires category_id from get_market_categories - call that method first to find the right category.' It names the prerequisite tool ('get_market_categories') and specifies when to use this tool ('for competitive landscape analysis'), offering clear context for selection over alternatives like general domain info tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully communicates that this is a read-only operation ('check available audit credits') and explicitly states it doesn't consume API credits, which is crucial behavioral information. However, it doesn't mention response format, error conditions, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly sized at two sentences with zero waste. The first sentence states the purpose and scope, while the second provides crucial usage guidance and behavioral information. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no output schema, the description provides excellent context about what information is returned (audit credit types) and important behavioral characteristics (non-consumptive). The main gap is the lack of information about response format, but given the tool's simplicity, this is a minor omission.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the baseline would be 3. The description adds value by explaining the semantic context of what credits are being checked ('audit credits including one-page audit, JavaScript scanning, and page crawl limits'), which provides useful information beyond the empty parameter schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('check available audit credits') and resources ('one-page audit, JavaScript scanning, and page crawl limits'). It distinguishes from sibling tools like 'get_credits_stats' by focusing specifically on audit-related credits rather than general credit statistics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('Use this before running site audits to verify available resources') and includes an important exclusion ('This method does not consume API credits'), which helps differentiate it from potentially credit-consuming operations among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it 'does not consume API credits,' which is crucial for understanding its impact. However, it lacks details on rate limits, authentication needs, or response format, leaving some gaps in transparency for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: two sentences that efficiently convey the tool's function, usage context, and a key behavioral trait ('does not consume API credits'). Every sentence adds value without redundancy, making it easy to understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple, no parameters) and lack of annotations and output schema, the description is fairly complete. It covers purpose, usage, and a critical behavioral aspect. However, it could improve by detailing the response format or error handling, which would enhance completeness for a tool with no structured output documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description does not need to add parameter information, and it appropriately focuses on the tool's purpose and behavior. A baseline of 4 is given as it compensates well for the lack of parameters by providing clear context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check available API credits, usage statistics, account information, and browser plugin limits.' It uses specific verbs ('check') and resources ('API credits,' 'usage statistics,' etc.), making the function evident. However, it does not explicitly differentiate from sibling tools like 'get_credits_for_audit_stats,' which might offer overlapping functionality, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage: 'Perfect for monitoring API usage and planning resource-heavy operations.' This gives practical guidance on when to use the tool. However, it does not specify when not to use it or name alternatives among siblings, such as 'get_credits_for_audit_stats,' which could be relevant for more specific scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what data is returned (visibility, traffic, relevance), it doesn't address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or error conditions. For a tool with no annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and key return data. Every word earns its place with no redundancy or unnecessary elaboration, making it easy for an agent to quickly understand the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, nested objects, no output schema, and no annotations), the description is minimally adequate. It states what the tool does but lacks crucial context about when to use it, behavioral characteristics, and how it differs from similar sibling tools. The absence of output schema means the description should ideally provide more detail about return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'domain' and the returned data fields but provides no additional context about parameter interactions, default behaviors, or practical usage examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a list of competitor domains for a given domain, including visibility, traffic, and relevance.' It specifies the verb ('Get'), resource ('competitor domains'), and key data points returned. However, it doesn't explicitly differentiate from sibling tools like 'get_url_competitors' or 'get_keyword_competitors', which appear to serve similar competitive analysis functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'get_url_competitors' and 'get_keyword_competitors' available, there's no indication of whether this tool is for domain-level analysis specifically, nor any prerequisites or context for its application.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions what data is returned (keywords with position, traffic, difficulty analysis), it lacks critical behavioral information: whether this is a read-only operation, any rate limits, authentication requirements, data freshness, or pagination behavior (despite having page/size parameters). The description doesn't contradict annotations since none exist, but it's insufficient for a complex 11-parameter tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. Every element ('Get keywords that domain ranks for', 'Google search results', 'position, traffic, difficulty analysis', 'comprehensive SEO insights and performance metrics') contributes meaning without redundancy. However, it could be slightly more structured by separating core function from included metrics.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters, nested objects, no annotations, and no output schema, the description is inadequate. It doesn't explain the relationship between parameters (e.g., how filters interact with keywords arrays), doesn't describe the return format despite rich filtering capabilities, and provides no behavioral context. The description fails to compensate for the lack of structured metadata about this sophisticated analysis tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema descriptions. It mentions 'comprehensive SEO insights and performance metrics' which aligns with the detailed filter and sort parameters, but provides no additional semantic context about parameter usage or relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get keywords that domain ranks for in Google search results' with specific details about included metrics (position, traffic, difficulty analysis, SEO insights, performance metrics). It distinguishes from siblings like 'get_keywords' by focusing on domain-specific ranking analysis, though it doesn't explicitly compare to alternatives like 'get_domain_uniq_keywords'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for keyword and domain analysis (e.g., 'get_keywords', 'get_domain_uniq_keywords', 'get_url_keywords'), there's no indication of when this specific domain-focused keyword ranking tool is preferred over other keyword-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by describing what the tool returns ('keyword count by country, regional performance comparison, and identifies primary market'), which is valuable behavioral information. However, it doesn't mention potential limitations like rate limits, authentication requirements, data freshness, or error conditions that would be helpful for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the most critical information ('REQUIRED FIRST STEP...'). All sentences earn their place by explaining purpose, usage context, and return values. It could be slightly more concise by combining some clauses, but overall it's well-structured with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (regional analysis with 3 parameters) and no output schema, the description does a good job explaining what the tool returns. However, with no annotations and no output schema, it could benefit from more detail about the format/structure of the return data (e.g., is it a list, object, or table?) and any behavioral constraints. The description is mostly complete but has minor gaps in fully documenting the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description doesn't add specific parameter semantics beyond what's in the schema (domain, sort, order). It mentions 'domain keyword presence' which relates to the domain parameter, but doesn't provide additional context about parameter interactions, default behaviors, or practical usage examples that would enhance understanding beyond the schema's technical definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('determines optimal regional database', 'analyzing domain keyword presence', 'identifies which regional database contains the most keyword data') and distinguishes it from siblings by positioning it as a 'REQUIRED FIRST STEP ONLY IF DOMAIN ANALYSIS' for subsequent domain analysis tools. It goes beyond the name to explain the core function of regional optimization for keyword data analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it states this is a 'REQUIRED FIRST STEP ONLY IF DOMAIN ANALYSIS' and explains its role in 'ensuring subsequent analysis uses the correct market context.' This clearly indicates when to use this tool (as a prerequisite for domain analysis) versus when not to use it (for non-domain analysis tasks), and implies alternatives would be other domain analysis tools that follow this initial step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get' implies a read-only operation, it doesn't address critical aspects like rate limits, authentication requirements, data freshness, or error handling. For a tool that likely queries external SEO databases, this lack of behavioral context is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place: 'Get' (action), 'comprehensive SEO information' (scope), 'multiple domains' (target), and the four specific data types. There's no wasted verbiage or redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It tells what the tool does but lacks context about when to use it, behavioral constraints, or output format. For a tool that likely returns rich SEO data, the description should provide more guidance about the nature and structure of returned information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific semantics beyond what's in the schema. It mentions 'multiple domains' which aligns with the 'domains' parameter, but provides no additional context about the 'se' or 'filters' parameters. Baseline 3 is appropriate when the schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get comprehensive SEO information for multiple domains including visibility, keywords, traffic, and dynamics.' It specifies the verb ('Get'), resource ('multiple domains'), and scope of information. However, it doesn't explicitly differentiate from sibling tools like 'get_domain_keywords' or 'get_domain_competitors' that might provide overlapping or related SEO data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools focused on specific SEO aspects (e.g., 'get_domain_keywords', 'get_backlinks_summary'), there's no indication of when this comprehensive tool is preferred over more targeted ones. It mentions 'multiple domains' but doesn't specify prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns data ('Returns unique keywords'), implying a read-only operation, but doesn't mention any behavioral traits such as rate limits, authentication requirements, data freshness, or pagination behavior (though pagination parameters exist in the schema). This is inadequate for a tool with complex parameters and no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences that directly state the tool's purpose and provide a contextual analogy ('Equivalent to a Domain vs domain report'). It's front-loaded with the core functionality, though the second sentence could be more informative. There's no wasted text, making it efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, nested objects, no output schema, and no annotations), the description is insufficient. It doesn't explain the output format, pagination behavior, or how filters interact with domain comparisons. Without annotations or an output schema, the agent lacks critical context for proper tool invocation and result interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by implying the tool compares domains ('two domains' vs 'third domain'), but doesn't explain parameter interactions or provide additional context beyond what's in the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns unique keywords of two domains for which a third domain does not rank.' It specifies the verb ('returns'), resource ('unique keywords'), and scope ('two domains' vs 'third domain'). However, it doesn't explicitly differentiate from sibling tools like 'get_domain_keywords' or 'get_keywords', which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'Equivalent to a Domain vs domain report,' which hints at a use case but doesn't specify prerequisites, exclusions, or compare it to sibling tools like 'get_domain_keywords' or 'get_keywords_info' that might serve similar purposes. This leaves the agent without clear usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it performs analysis (not just data retrieval), discloses cost implications ('Each URL costs 1 API credit, minimum 1 credit per request'), and implies pagination through parameters. However, it doesn't specify rate limits, authentication needs, or what happens with invalid domains, leaving some gaps for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by analysis details and cost information. Every sentence adds value, with no redundant or vague phrasing. It could be slightly more structured by separating purpose from operational details, but it remains efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, nested objects, no output schema, and no annotations), the description is moderately complete. It covers purpose, analysis scope, and costs, but lacks details on output format, error handling, or performance characteristics. Without annotations or output schema, the description should do more to guide the agent on what to expect from the tool's behavior and results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no specific parameter semantics beyond what's in the schema—it doesn't explain how 'domain' interacts with 'filters' or how 'sort' affects the analysis. With high schema coverage, the baseline is 3, and the description doesn't compensate with additional insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs and resources: 'Get URLs within a domain and keyword count for each URL. Analyze URL structure, performance distribution, and identify top-performing pages.' It distinguishes itself from siblings by focusing on domain URL analysis with keyword counts, unlike tools like 'get_domain_keywords' or 'get_url_keywords' which focus on keywords rather than URL enumeration and analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for analyzing domain URLs and performance, but does not explicitly state when to use this tool versus alternatives like 'get_url_summary_traff' or 'get_top_pages_by_backlinks'. It mentions API credit costs, which provides some operational context, but lacks clear guidance on scenarios where this tool is preferred over other domain or URL analysis tools in the sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool lists domains and shows analysis metrics, it doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or pagination behavior. For a tool with 5 parameters and complex filtering, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences that convey the core functionality and output details without unnecessary elaboration. It's appropriately front-loaded with the main purpose. However, it could be slightly more concise by combining related concepts.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters, complex nested filtering, no annotations, and no output schema, the description is inadequate. It doesn't explain the return format, error conditions, performance characteristics, or how the various metrics (visibility, traffic, relevance) are calculated or interpreted. The description leaves too many contextual gaps for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions 'keyword' and implies filtering capabilities through 'detailed competitor analysis', but adds minimal semantic value beyond the schema. With 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description doesn't explain parameter interactions, default behaviors, or practical usage examples beyond what's in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Lists the domains that rank'), target resource ('for the given keyword in Google top-20 results'), and output details ('detailed competitor analysis including visibility metrics, traffic data, keyword dynamics, and relevance scores'). It distinguishes from siblings like 'get_domain_competitors' by focusing on keyword-based competitor analysis rather than domain-based.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_domain_competitors' or 'get_keyword_top', nor does it mention prerequisites, exclusions, or specific contexts. It simply states what the tool does without indicating when it's the appropriate choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool returns data (implying read-only) and specifies the scope (top-100 results), but doesn't mention rate limits, authentication needs, pagination behavior, data freshness, or error conditions. For a tool with 4 parameters and complex sorting options, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core function, and the second details the return metrics. It's front-loaded with the main purpose and avoids unnecessary words, though it could be slightly more concise by integrating the two sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters with nested sorting, no output schema, no annotations), the description is minimally adequate. It covers the purpose and return metrics but lacks behavioral context, usage guidelines, and output format details. The high schema coverage helps, but for a data retrieval tool with sibling alternatives, more contextual guidance would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the 'keyword' parameter is for analysis and mentioning metrics that correlate with sortable fields, but doesn't explain parameter interactions or provide usage examples. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'shows Google's top-100 search results for the analyzed keyword' and specifies it returns detailed domain metrics like visibility, organic/PPC keywords count, SDR score, and backlink metrics. It uses specific verbs ('shows', 'returns') and identifies the resource (search results/domains), but doesn't explicitly differentiate from sibling tools like 'get_keyword_top' or 'get_keyword_top_urls'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'get_keyword_top' (which might return less detailed data) or 'get_keyword_top_urls' (which might focus on URLs rather than domains). Usage is implied only by the tool's name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the output format ('displays its search volume, CPC, and competition level') but lacks critical details like pagination behavior, rate limits, authentication requirements, error conditions, or whether this is a read-only operation. For a tool with 8 parameters and complex filtering, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two clear sentences. The first sentence establishes the core purpose, and the second specifies the output metrics. There's no wasted verbiage, though it could be slightly more structured for complex parameter guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters, complex nested objects, no annotations, and no output schema, the description is incomplete. It doesn't explain the relationship between parameters, how filtering/sorting works, what the response structure looks like, or any behavioral constraints. The agent would struggle to use this effectively without trial and error.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions the 'keyword' parameter implicitly and hints at output metrics that correlate with some filterable fields. No additional parameter semantics are provided beyond what's in the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Shows organic keywords related to the researched keyword for which domains rank in Google top 100.' It specifies the action ('shows'), resource ('organic keywords'), and scope ('Google top 100'), but doesn't explicitly differentiate from sibling tools like 'get_related_keywords' or 'get_keyword_suggestions'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, suggest scenarios for usage, or indicate prerequisites. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions what metrics are returned, it doesn't describe important behavioral aspects like rate limits, authentication requirements, whether this is a read-only operation, potential costs/credits usage, or how results are structured/paginated. For a tool with 5 parameters and complex filtering/sorting capabilities, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences that efficiently convey the tool's purpose and scope. The first sentence clearly states what the tool does, and the second sentence elaborates on the types of metrics included. There's no wasted language or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters (including complex nested objects for sorting and filtering), no annotations, and no output schema, the description is insufficient. It doesn't explain the relationship between parameters, how filtering/sorting affects results, what the output format looks like, or any limitations/constraints. The agent would need to rely heavily on the schema alone to understand how to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'multiple keywords' (implied by the array parameter) and 'keyword intents' (covered in the schema's withIntents description). The description doesn't provide additional context about parameter interactions or usage patterns that aren't already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get keyword overview showing volume, CPC, competition level, difficulty, and additional metrics for multiple keywords.' It specifies the verb ('Get') and resource ('keyword overview'), and lists key metrics. However, it doesn't explicitly differentiate from sibling tools like 'get_keywords' or 'get_keyword_top', which appear to be related keyword analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'get_keywords', 'get_keyword_top', and 'get_keyword_suggestions', there's no indication of what makes this tool unique or when it's the appropriate choice. The description mentions 'comprehensive analysis' but doesn't clarify how this differs from other keyword tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool 'shows' and 'returns' suggestions, it doesn't address important behavioral aspects: whether this is a read-only operation, if there are rate limits, authentication requirements, what happens with invalid inputs, or how the full-text search behaves. The description provides basic functional information but lacks operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences that convey the core functionality. The first sentence states what the tool does, and the second specifies what it returns. There's no wasted language, though it could be slightly more front-loaded by mentioning the geographic information aspect earlier.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 parameters, nested objects, no output schema, no annotations), the description is minimally adequate. It explains what the tool does at a high level but lacks details about the return format, pagination behavior, error handling, or how the suggestions are ranked/ordered. Without annotations or output schema, more behavioral context would be helpful for proper tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it mentions 'keyword' generically but doesn't explain the 'se' parameter's country codes or the 'filters' object. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Shows search suggestions for the keyword you requested' and specifies that these suggestions are 'found by the full-text search' and include 'geographic names information'. It uses specific verbs ('shows', 'returns') and identifies the resource (keyword suggestions), but doesn't explicitly differentiate from sibling tools like 'get_keywords' or 'get_related_keywords'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like 'get_keywords', 'get_keywords_info', and 'get_related_keywords', there's no indication of when this specific keyword suggestion tool is appropriate versus those other keyword-related tools. No prerequisites, exclusions, or comparative context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool is 'deprecated but still functional,' which is useful context about its lifecycle, but fails to describe critical behaviors like rate limits, authentication needs, response format, pagination, or error handling for a tool with complex filtering parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core functionality and return data, the second adds important behavioral context (deprecation). It's front-loaded with key information and has no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters with nested objects, no output schema, no annotations), the description is minimally adequate. It covers the core purpose and deprecation status but lacks details on output structure, error cases, or integration with sibling tools, leaving gaps for an AI agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific semantics beyond implying keyword analysis and Google search results, which are already covered by parameter descriptions. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Shows Google's top-100 search results') and resources ('for the analyzed keyword'), and distinguishes it from siblings like 'get_keyword_full_top' by specifying it returns position data, URLs, domains, subdomains, and SERP feature types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_keyword_full_top' or 'get_keyword_top_urls', nor does it mention prerequisites or exclusions. The only contextual note is that it's deprecated but functional, which is not usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what data is returned ('URLs with keyword count, estimated traffic, and Facebook shares') but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior beyond parameters, or potential errors. For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured in two sentences: the first states the tool's purpose and selection criteria, and the second lists the returned data fields. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, no annotations, no output schema), the description is moderately complete. It covers the tool's purpose and output fields but lacks behavioral context, usage guidelines, and detailed parameter explanations. For a data retrieval tool in a suite with many siblings, it should provide more guidance on when to use it and how results are structured, but it meets a basic threshold.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal parameter semantics beyond the input schema, which has 100% coverage with detailed descriptions for all parameters. It implies the 'keyword' parameter is used for searching and mentions output fields ('keyword count, estimated traffic, Facebook shares') that relate to sorting options, but doesn't explain parameter interactions or provide examples. With high schema coverage, the baseline is 3, and the description doesn't add substantial value to elevate it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns website pages that rank for the largest amount of the analyzed keyword variations and have the highest traffic.' It specifies the verb ('returns'), resource ('website pages'), and criteria ('rank for keyword variations with highest traffic'). However, it doesn't explicitly differentiate from sibling tools like 'get_keyword_top' or 'get_keyword_full_top', which appear related but have unclear distinctions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a project or specific data setup, or compare it to siblings like 'get_keyword_top' or 'get_keyword_full_top'. The usage context is implied but not explicitly stated, leaving the agent to infer based on the tool name and description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions sorting and filtering aspects but lacks critical information such as whether this is a read-only operation, potential rate limits, authentication requirements, pagination behavior beyond parameters, or what the output format looks like. The description is insufficient for a tool with 7 parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and includes a practical usage tip. Every part earns its place with no wasted words, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits like safety, performance, or output format, and while it hints at parameter usage, it does not compensate for the absence of structured behavioral information, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds minimal value by implying the 'query' parameter is for analysis and suggesting a specific use of 'sort' and 'order' ('use sort by check desc'), but it does not provide additional meaning beyond what the schema already specifies for parameters like 'additionalFilters' or 'page'/'size'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('list of lost backlinks'), specifying what data is returned (linking pages, target pages, link attributes, deletion dates) and the purpose (domain or URL analysis). It effectively distinguishes from siblings like 'get_active_backlinks' by focusing on lost rather than active backlinks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('for domain or URL analysis') and includes a specific usage tip ('use sort by check desc to get recently lost backlinks'), but it does not explicitly state when not to use it or name alternatives among the many sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by describing the return format ('hierarchical categories like /Arts & Entertainment/TV & Video/Online Video with their IDs e.g. .2.13.1.'), the scale ('1000+ categories'), and that it's a foundational lookup operation. It doesn't mention rate limits or authentication needs, but provides substantial behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tightly focused sentences with zero waste: first states purpose and scale, second provides usage guidance, third describes return format and connection to other tools. Every sentence earns its place and information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter lookup tool with no annotations and no output schema, the description provides complete context: purpose, scale, usage sequence, return format with examples, and connection to downstream tools. No additional information is needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the description correctly states 'No parameters required - returns all available market categories' (mirroring the schema description). With 100% schema coverage and no parameters, the baseline would be 4, and the description appropriately confirms this.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get complete list'), resource ('market research categories'), and scope ('1000+ categories'). It distinguishes from sibling tools like 'get_category_top_domains' by explaining this tool provides category IDs needed for that other tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use this method first to find the appropriate category_id for your analysis' and mentions the specific sibling tool 'get_category_top_domains' that requires the output. This provides clear when-to-use guidance and references an alternative tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the API source (Serpstat) and return data (referring domains with metrics and filtering), but fails to disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, pagination behavior beyond parameters, or error handling. For a tool with 6 parameters and no annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('Get a list of referring domains') and includes key details (API source, return metrics, filtering). There's no wasted text, but it could be slightly more structured by separating functional and contextual elements. Overall, it's appropriately sized and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is moderately complete. It covers the purpose and high-level functionality but lacks details on behavioral aspects like pagination, errors, or API constraints. Without annotations or an output schema, the description should do more to compensate, but it provides a basic foundation that's adequate yet with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter well-documented in the schema (e.g., 'query' as 'Domain to analyze for referring domains'). The description adds minimal value beyond the schema by mentioning 'filtering options' and 'comprehensive backlink analysis', which loosely relates to parameters like 'sort' and 'searchType', but doesn't provide additional syntax, format, or usage details. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get a list of referring domains using Serpstat API' with specific resources (referring domains) and metrics (domain rank, referring pages count). It distinguishes from siblings like 'get_active_backlinks' or 'get_backlinks_summary' by focusing on domains rather than individual backlinks or summaries. However, it doesn't explicitly contrast with all similar tools (e.g., 'get_active_outlink_domains'), keeping it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for 'comprehensive backlink analysis' with filtering options, suggesting it's for detailed domain-level backlink insights. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_backlinks_summary' or 'get_active_backlinks', and doesn't mention prerequisites or exclusions. The context is clear but not fully directive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it indicates this is a read operation ('shows'), it doesn't mention important behavioral aspects like rate limits, authentication requirements, data freshness, pagination behavior (beyond what the schema indicates), or whether this is a computationally expensive operation. For a tool with complex filtering and sorting capabilities, this represents significant gaps in behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first states the core purpose, the second enumerates the data returned. It's appropriately sized and front-loaded with the essential information. Every sentence earns its place, though it could be slightly more concise by integrating the data details into the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters (including complex nested objects), no annotations, and no output schema, the description is adequate but has clear gaps. It explains what data is returned but not the format or structure. The combination of good schema coverage and a clear purpose statement makes this minimally viable, but more behavioral context would be needed for optimal agent understanding given the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds minimal value beyond the schema - it mentions the 'researched keyword' which maps to the 'keyword' parameter, but doesn't provide additional context about parameter usage or relationships. The baseline of 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Shows all search queries semantically related to the researched keyword' with specific details about the data displayed (search volume, CPC, competition, etc.). It uses a specific verb ('shows') and identifies the resource ('related keywords'), but doesn't explicitly distinguish it from sibling tools like 'get_keyword_suggestions' or 'get_keywords'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With multiple keyword-related tools in the sibling list (get_keyword_suggestions, get_keywords, get_keywords_info), there's no indication of when this semantic relationship tool is preferred over other keyword discovery methods. The description only states what it does, not when to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: returns large datasets (full top-100 for each keyword/date), includes competitor analysis with historical data, does not consume API credits, and warns about response size management. It lacks details on error handling or rate limits, but covers essential operational traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose, followed by important warnings and usage tips. Every sentence earns its place by adding critical information about dataset size, recommendations, filtering, tagging, and API credit impact, with no redundant or verbose content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (10 parameters, no output schema, no annotations), the description does a strong job by explaining the tool's purpose, behavioral traits, and usage guidance. It could improve by detailing the output structure (e.g., format of returned data) since there's no output schema, but it adequately covers input handling and operational context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds marginal value by mentioning keyword tagging (related to 'withTags') and filtering recommendations (implied for 'dateFrom', 'dateTo', 'keywords'), but does not provide significant additional semantics beyond what the schema descriptions already cover.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get complete Google top-100 SERP history for tracked keywords in a rank tracker project.' It specifies the verb ('Get'), resource ('SERP history'), and scope ('tracked keywords in a rank tracker project'), distinguishing it from siblings like 'get_rt_project_url_serp_history' which focuses on URLs rather than keywords.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context with warnings about large datasets and recommendations for pageSize (20-50), plus advice to use date and keyword filters to reduce response size. It mentions keyword tagging for grouping/filtering but does not explicitly state when NOT to use this tool or name specific alternatives among siblings, though it implies it's for keyword-based SERP history.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool as a read operation ('Get') and explicitly states 'does not consume API credits', which is crucial behavioral context not inferable from the schema. However, it lacks details on error handling, rate limits, or authentication needs, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the core purpose and efficiently lists the returned data fields. The second sentence adds critical behavioral information without redundancy. Every part earns its place, making it appropriately sized and zero-waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is largely complete: it specifies the purpose, data returned, and key behavioral trait (no API credit consumption). However, it lacks output format details (e.g., structure of the list) and error scenarios, which could be helpful despite the absence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the single parameter 'projectId' documented as 'Project identifier'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('list of regions configured for a rank tracker project'), specifying the exact data returned (region ID, status, SERP type, device type, search engine, location details). It distinguishes from siblings like 'get_rt_projects_list' (lists projects) and 'get_rt_project_status' (gets project status), making the purpose specific and well-differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when needing region details for a rank tracker project, but provides no explicit guidance on when to use this tool versus alternatives like 'get_domain_regions_count' or 'get_rt_project_status'. It mentions 'does not consume API credits', which offers some context but lacks clear when/when-not scenarios or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a read operation (implied by 'Get'), specifies the data fields returned, and importantly notes 'This method does not consume API credits'—a critical piece of operational context not inferable from the schema alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose, followed by an important behavioral note. Every word earns its place with zero waste, making it highly efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (simple list retrieval), 100% schema coverage, and no output schema, the description is nearly complete: it states purpose, data fields, and a key behavioral trait (no API credit cost). It lacks details on pagination behavior or error handling, but for this context, it's sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema fully documents both parameters (page and pageSize). The description adds no parameter-specific information beyond what the schema provides, resulting in the baseline score of 3 for adequate but no extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'list of rank tracker projects', specifying the exact data fields included (project ID, name, domain, creation date, tracking status). It distinguishes from siblings like 'list_projects' by explicitly mentioning 'rank tracker' projects and the no-API-credit consumption.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('Get a list of rank tracker projects') and implicitly suggests an alternative (e.g., 'list_projects' might be for different project types). However, it doesn't explicitly state when not to use it or name specific alternatives, keeping it at a 4.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully communicates key behavioral traits: it's a read-only status check (implied by 'Get the current status'), it doesn't consume API credits (explicitly stated), and it serves as a prerequisite check before data requests. However, it doesn't mention potential rate limits, authentication requirements, or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with three sentences that each serve distinct purposes: stating the tool's function, providing usage guidance, and disclosing cost behavior. There's zero wasted language, and the most important information (what the tool does) comes first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple status-check tool with 2 parameters and 100% schema coverage, the description provides excellent context. It explains the tool's purpose, when to use it, and key behavioral aspects (no API credit consumption). The main gap is the lack of output schema, but for a status check tool, the description adequately conveys what information will be returned (parsing status/readiness).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (projectId and regionId are clearly defined there). The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get the current status of position updates (parsing)') and identifies the target resource ('rank tracker project and region'). It distinguishes itself from sibling tools by focusing on status checking rather than data retrieval or project management operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides usage guidance: 'Use this to check if data is ready before requesting results.' This gives clear context for when to use this tool (as a readiness check) versus when to use data retrieval tools. It also specifies 'This method does not consume API credits,' which helps with cost-aware decision making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates key behavioral traits: it's a read operation (implied by 'Get'), specifies what data is returned ('historical position data, search volumes, and optional keyword tags'), and importantly discloses that 'This method does not consume API credits' - a critical operational detail not captured elsewhere.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with three sentences that each serve distinct purposes: stating the core functionality, differentiating from alternatives, and providing additional behavioral context. There is no wasted text, and the most critical information (what the tool does and how it differs) appears first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters and no output schema, the description provides strong contextual completeness. It clearly explains the tool's purpose, differentiation, and key behavioral aspects. The main gap is the lack of output format details (what the return structure looks like), but given the detailed parameter schema and clear functional description, it's mostly sufficient for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter-specific information beyond what's in the schema, mentioning only that it returns data 'where your specified domain/URL ranks' (related to the 'domain' parameter). This meets the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get ranking history'), resource ('YOUR domain's positions'), and scope ('across all tracked keywords'). It explicitly distinguishes this tool from its sibling 'get_rt_project_keyword_serp_history' by contrasting their outputs (domain-only vs. full top-100), providing excellent differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('Perfect for tracking your own performance over time without competitor noise') and directly names the alternative sibling tool ('Unlike get_rt_project_keyword_serp_history'). It also includes a practical usage scenario that helps the agent understand the appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's 'lightweight', 'does not consume API credits', and lists specific return fields like scores and flags. However, it doesn't mention error handling, rate limits, or authentication needs, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with two sentences: the first front-loads the purpose and return values, and the second provides usage guidelines and alternatives. Every sentence adds value without redundancy, making it appropriately sized and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is largely complete: it explains the purpose, usage, behavior, and return values. However, without an output schema, it could benefit from more detail on return value formats or error cases, but it covers the essentials well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting the 'reportId' parameter. The description adds no additional parameter details beyond what the schema provides, so it meets the baseline of 3 without compensating for any gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Get quick summary') and resource ('a single audit'), distinguishing it from siblings like 'get_site_audit_deteailed_report' and 'get_site_audit_results_by_categories' by emphasizing it's for 'dashboard display' and 'lightweight'. It explicitly contrasts with detailed analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('for dashboard display', 'lightweight method') and when to use alternatives ('For detailed analysis use get_site_audit_results_by_categories or get_site_audit_deteailed_report'), including a clear exclusion for detailed scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the return structure (categories array with errors containing specific fields like key, priority, countAll, countNew, countFixed) and explicitly states 'Does not consume API credits,' which is valuable operational context. However, it doesn't mention potential rate limits, error handling, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose. Every sentence adds value: the first defines the tool, the second details the return structure, the third explains parameter usage, the fourth links to sibling tools, and the fifth provides API credit information. It could be slightly more structured with bullet points for the return fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (detailed error analysis with comparison capabilities) and lack of output schema, the description does an excellent job explaining the return structure and how to use the parameters. It covers the key aspects needed for an agent to understand what the tool does and how to interpret results. The only minor gap is the absence of explicit error handling or pagination details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the semantic purpose of compareReportId ('to track changes between audits') and how it relates to the output fields (countNew, countFixed). This contextual information goes beyond the schema's technical documentation of parameter types and constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get COMPLETE error breakdown organized by categories') and resource ('detailed audit analysis'). It distinguishes from siblings by specifying it's the 'Main method for detailed audit analysis' and mentions related tools like get_site_audit_history and get_site_audit_pages_spec_errors for different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool vs alternatives: 'Main method for detailed audit analysis' establishes primary use, and it specifies when to use sibling tools ('Use error.key with get_site_audit_history... or get_site_audit_pages_spec_errors...'). It also explains the purpose of the compareReportId parameter for tracking changes between audits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by explaining the tool's limitations (only works for hierarchical errors, not page-level ones), its dependency on a previous step's output (crc parameter), and a key operational detail ('Does not consume API credits'). However, it lacks information on error handling beyond one example, response format, or pagination behavior, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose. It uses clear bullet-like points and an example to illustrate usage. While slightly verbose due to the example and clarifications, every sentence adds value (e.g., distinguishing error types, explaining parameter linkage), making it efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters, no annotations, and no output schema, the description does a good job covering the essential context: purpose, usage workflow, limitations, and key parameter semantics. It explains the tool's role in a two-step process and its constraints. However, it doesn't describe the output format or full error scenarios, which would be helpful given the lack of structured output information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds value by explaining the 'crc' parameter's source and purpose (must match urlCrc/imageCrc from previous method), but doesn't provide additional context for other parameters like 'reportId', 'errorName', or pagination parameters. Given the high schema coverage, a baseline score of 3 is appropriate as the description compensates partially but not extensively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it's a drill-down step that shows WHERE a problematic element is used, specifically for hierarchical errors like images, scripts, and links. It distinguishes itself from siblings by explicitly mentioning it's 'Step 2' and referencing 'get_site_audit_pages_spec_errors' as the previous step, making its role in a workflow evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: as a follow-up to 'get_site_audit_pages_spec_errors' for hierarchical errors, using a crc from that response. It also clearly states when NOT to use it: for page-level errors like 'no_desc, no_title, h1_missing', specifying it returns an error in those cases. This covers both usage context and exclusions effectively.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a read-only operation (implied by 'track' and 'returns'), has no API credit consumption ('Does not consume API credits'), and clarifies scope ('across all audits in project'). However, it doesn't mention rate limits, authentication needs, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with zero waste: it opens with the core purpose, states the return format, lists three specific use cases, provides parameter guidance with an example, and ends with a key operational note. Every sentence adds essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with no annotations and no output schema, the description is quite complete: it covers purpose, usage, parameters, and behavioral context. However, it could be slightly improved by explicitly stating the tool's read-only nature or mentioning pagination behavior given the limit/offset parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema: it explains that errorName must be from 'the fixed enum list' and provides an example ('errorName='no_desc' shows trend of pages without meta description'), but doesn't elaborate on projectId, limit, or offset parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('track how a SPECIFIC error type changed over time'), resource ('across all audits in project'), and output format ('Returns array with reportId, date, and count for each audit'). It distinguishes from siblings like 'get_site_audit_deteailed_report' by focusing on temporal trends rather than detailed snapshots.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides three use cases ('verify if fixes are working', 'monitor if errors are growing', 'track specific problem areas'), specifies when to use it (for tracking error trends over time), and references a related tool ('get_site_audit_deteailed_report') for context on errorName values.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully describes key behavioral traits: the tool is part of a drill-down workflow, returns specific fields (url, urlCrc/imageCrc, count), explains how the CRC is used for deeper analysis, describes the three mode options, and importantly discloses that it 'Does not consume API credits' - a crucial operational detail not evident from the schema alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized. It starts with the core purpose, explains the return values, describes the mode parameter, provides a workflow example, and ends with the API credit disclosure. Every sentence serves a clear purpose, though it could be slightly more concise by combining some explanatory elements.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters, no annotations, and no output schema, the description does an excellent job of providing context. It explains the return format, the workflow relationship with sibling tools, the meaning of key parameters, and important operational details (no API credit consumption). The main gap is not explicitly describing pagination behavior despite having limit/offset parameters, though the schema covers these.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds some value by explaining the 'mode' parameter options ('all', 'new', 'solved') and their meanings, but doesn't provide additional semantic context beyond what's in the schema descriptions. This meets the baseline of 3 when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get list of elements (pages/images) with specific error' - a specific verb (get) and resource (elements with specific error). It distinguishes from sibling tools like 'get_site_audit_elements_with_issues' by explaining this is the first drill-down step that provides CRCs for deeper analysis with that sibling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool vs alternatives: 'Example flow: get pages with 'image_no_alt' error → get imageCrc → use with get_site_audit_elements_with_issues to see which pages use that image.' It also explains the relationship with the sibling tool and provides a concrete workflow example.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it 'does not consume API credits and does not require projectId,' specifies it 'returns server-side recommended defaults,' and details differences from project settings (e.g., domain='', name='', pagesLimit=5000). This covers safety, cost, and output behavior comprehensively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by usage scenarios, workflow steps, and behavioral details. Each sentence earns its place by providing essential information without waste, such as clarifying differences from project settings and API credit implications, making it efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (retrieving default settings for project creation) and lack of annotations or output schema, the description is complete. It explains the purpose, usage workflow, behavioral traits (no credits required, no projectId), and key output fields, ensuring an AI agent has all necessary context to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on usage and output semantics, mentioning key fields in the returned object (domain, name, pagesLimit, scheduleRepeatOption) without redundancy. A baseline of 4 is applied since no parameters exist, and the description adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool retrieves 'DEFAULT TEMPLATE settings for creating new projects' and clarifies it's 'NOT settings of existing project,' providing a specific verb (get) and resource (default template settings). It clearly distinguishes from sibling tools like 'get_site_audit_settings' by emphasizing defaults versus existing project data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: to 'populate new project form with sensible defaults, avoid hardcoding values, ensure settings stay current.' It outlines a clear workflow (call this, modify returned object, call set_site_audit_settings) and names the alternative tool ('set_site_audit_settings') for saving modifications.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns aggregated statistics (not detailed data), lists the fixed categories, specifies the output structure (highCount, mediumCount, lowCount, informationCount), and notes it does not consume API credits. However, it lacks details on error handling, rate limits, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by details on output structure, usage context, fixed categories, and a clear alternative. Each sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (aggregated statistics with fixed categories) and lack of annotations and output schema, the description does a good job of covering purpose, usage, and output details. It could be more complete by mentioning potential errors or response formats, but it adequately informs the agent for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'reportId' clearly documented. The description does not add any additional semantic context about the parameter beyond what the schema provides, such as how to obtain a reportId or its format. Since schema coverage is high, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('AGGREGATED error statistics by category'), specifying it returns sum of errors grouped by priority for each category. It explicitly distinguishes from its sibling 'get_site_audit_deteailed_report' by stating this is for 'quick overview' versus 'specific error breakdown'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('Use this for quick overview to identify problematic categories') and when to use an alternative ('For specific error breakdown use get_site_audit_deteailed_report'). It also mentions that it 'Does not consume API credits,' which is a practical consideration for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does so effectively. It discloses critical behavioral traits: error conditions ('Returns error...'), operational constraints ('Does not consume API credits'), and clarifies the nature of returned data ('INPUT configuration, not OUTPUT'). It doesn't cover all possible edge cases like rate limits, but provides substantial context beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with zero waste: every sentence adds critical information. It's front-loaded with the core purpose, followed by constraints, error conditions, differentiation from alternatives, and operational details. No redundant or vague phrasing exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is complete. It covers purpose, usage constraints, error handling, data nature, and operational impact. For a read-only tool with good parameter documentation, no additional information is needed for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single parameter 'projectId' adequately. The description doesn't add any parameter-specific information beyond what's in the schema, but doesn't need to given the high coverage. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get CONFIGURED URL list for scanning') and resource ('URL list'), distinguishing it from siblings like 'get_site_audits_list' (actual scan results) and 'get_site_audit_settings' (settings). It explicitly clarifies this shows 'INPUT configuration, not OUTPUT of what was scanned,' preventing confusion with result-oriented tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it specifies when to use ('ONLY works when scanSetting.type is 2 or 3'), when not to use (returns error 'Scan url list not found' when type=1), and names an alternative ('Check get_site_audits_list response for actual scanned page count'). This covers all key decision points for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and effectively discloses key behavioral traits: it specifies the tool is for reading ('Get'), not modifying, settings; lists return components (e.g., mainSettings, scan filters); and adds context like 'Does not consume API credits' for cost implications. However, it lacks details on error handling or response format, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by return details and usage guidance, all in three sentences with minimal waste. It could be slightly more structured by separating return values into a list, but overall it's efficient and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (read operation with detailed returns) and no annotations or output schema, the description does well by specifying return components and usage context. However, it doesn't fully explain the return structure or potential errors, leaving some completeness gaps for an agent to invoke it correctly without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single parameter 'projectId' with its type and constraints. The description does not add meaning beyond this, as it doesn't explain parameter usage or nuances. Baseline 3 is appropriate when the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('current configuration of EXISTING audit project'), specifying it retrieves settings rather than creating or modifying them. It distinguishes from sibling 'get_site_audit_project_default_settings' by contrasting existing vs. new projects, making the purpose specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly guides when to use this tool ('EXISTING audit project') vs. an alternative ('Compare with get_site_audit_project_default_settings which returns template for NEW projects'), providing clear context for selection. It also mentions 'Does not consume API credits' as a practical consideration for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it 'Does not consume API credits' (important cost/rate limit information), explains the purpose of the hasDetailData flag, and details what the response contains. However, it doesn't mention error conditions, authentication requirements, or pagination behavior beyond the limit/offset parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with purpose first, then output details, then behavioral notes, and ends with a practical tip. Every sentence adds value, though the output details section is somewhat dense. It could be slightly more streamlined but remains highly effective for its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a list-retrieval tool with no output schema, the description provides excellent coverage of what the response contains (reportId, date, SDO score, etc.) and important behavioral context (no API credit consumption). It lacks details about error responses and doesn't explicitly mention the pagination mechanism, but given the tool's relative simplicity and the comprehensive output description, it's quite complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions sorting by date but doesn't connect this to any parameters. The baseline of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Returns list of all audit reports') and resource ('for a project'), and distinguishes it from sibling tools by positioning it as the 'STARTING POINT for audit analysis' and noting that its reportId output should be used with other audit methods. This differentiates it from other audit-related tools like get_site_audit_deteailed_report or get_site_audit_results_by_categories.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it's the 'STARTING POINT for audit analysis' and specifies to 'Use reportId from response with other audit methods.' It also includes a practical tip: 'Sort by date to get most recent audit first.' This gives clear context for when to use this tool versus diving directly into detailed audit methods.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses that the tool returns data with specific fields (e.g., referring domain, threat type) and mentions the purpose ('identifying and removing harmful backlinks'), but lacks details on behavioral traits such as rate limits, authentication needs, pagination behavior, or error handling. The description adds some context but does not fully compensate for the absence of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with three sentences that are front-loaded: the first states the purpose, the second details return values, and the third explains importance. Each sentence adds value without redundancy, though it could be slightly more concise by integrating the second and third sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 parameters, no annotations, no output schema), the description is moderately complete. It covers the tool's purpose and return fields but lacks details on output structure, error cases, or usage constraints. Without annotations or output schema, the description should provide more behavioral context to be fully adequate for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'query' beyond 'analyzed domain' or clarify 'linkPerDomain' usage). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get malicious backlinks pointing to the analyzed domain') and resource ('backlinks from sites flagged for threats'). It distinguishes from siblings like 'get_active_backlinks' by specifying 'malicious' and 'threat' focus, making the differentiation explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('Essential for identifying and removing harmful backlinks that could damage domain reputation and SEO rankings'), suggesting when this tool is valuable. However, it does not explicitly state when to use it versus alternatives like 'get_active_backlinks' or 'get_lost_backlinks', nor does it provide exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool is 'fast' and provides 'brief' information, which adds useful context about performance characteristics. However, it doesn't disclose critical behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with invalid domains. For a tool with zero annotation coverage, this leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences that both add value. The first sentence states the core purpose, and the second provides important usage guidance. There's no redundant information, and the structure is front-loaded with the main functionality. It could be slightly more polished but earns a high score for efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (domain analysis with two parameters), no annotations, and no output schema, the description provides adequate but incomplete coverage. It explains what the tool does and when to use it, but lacks details about behavioral characteristics, error handling, and output format. The description is minimally viable but has clear gaps that could hinder effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with both parameters ('query' and 'searchType') well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema already provides. According to scoring rules, when schema coverage is high (>80%), the baseline score is 3 even without parameter details in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get TOP-10 anchors with the number of backlinks and referring domains for domain analysis.' It specifies the verb ('Get'), resource ('TOP-10 anchors'), and key metrics ('backlinks and referring domains'). However, it doesn't explicitly distinguish this tool from sibling 'get_anchors' (which likely returns more comprehensive anchor data), so it doesn't reach the highest clarity level.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'use this method if you need a fast brief way to get info about top 10 anchors.' This indicates when to use this tool (for quick, limited insights) versus potentially more comprehensive alternatives. However, it doesn't name specific alternative tools or explicitly state when NOT to use it, keeping it from a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API source and return content ('pages with the highest number of referring pages, domains, and IP addresses'), but lacks critical details like authentication requirements, rate limits, pagination behavior, error conditions, or whether this is a read-only operation. The description doesn't contradict annotations (none exist), but provides insufficient behavioral context for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences that directly state the tool's function and return value. It's front-loaded with the core purpose and avoids unnecessary elaboration. However, it could be slightly more structured by explicitly separating purpose from behavioral details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is incomplete. It doesn't address how results are formatted, what 'comprehensive backlink analysis' entails operationally, or any limitations. For a data retrieval tool with multiple configuration options, more context about output structure and usage constraints would be needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so all parameters are documented in the schema. The description adds no specific parameter semantics beyond what's in the schema—it doesn't explain how 'query' relates to 'searchType', what the sort fields mean in practice, or provide usage examples. With high schema coverage, the baseline is 3, and the description doesn't add meaningful value here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get leading pages by backlinks using Serpstat API' specifies the verb ('Get') and resource ('leading pages by backlinks'), and mentions the API source. It distinguishes from siblings like 'get_backlinks_summary' or 'get_referring_domains' by focusing on pages with highest backlink metrics, but doesn't explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives among the many sibling tools. It mentions 'comprehensive backlink analysis' but doesn't specify scenarios, prerequisites, or exclusions that would help an agent choose between this and similar backlink-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits. It explains data availability requirements (10+ keywords in top-10), error conditions ('Data not found' for new/low-traffic pages), input format requirements (URL must include https://), and operational details (API cost: 1 credit per result row). This goes well beyond basic functionality description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose. Each sentence adds important information about constraints, errors, and costs. However, the final sentence about API costs feels somewhat tacked on and could be better integrated with the preceding content about data availability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters, 100% schema coverage, but no annotations and no output schema, the description provides excellent context about behavioral constraints, error conditions, and cost implications. The main gap is the lack of information about return format or what 'competitor URLs' data structure looks like, which would be helpful given no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds some value by reinforcing the URL parameter requirements ('must include protocol https://') and linking URL quality to data availability, but doesn't provide significant additional parameter semantics beyond what's already well-documented in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns competitor URLs that rank for the same keywords in Google top-10.' It specifies the resource (competitor URLs), the context (Google top-10 rankings), and distinguishes it from siblings like 'get_domain_competitors' or 'get_keyword_competitors' by focusing on URL-level analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: 'Best results for established pages with significant organic traffic' and 'The analyzed URL must rank for 10+ keywords in top-10 to have competitor data available.' It also explains when it won't work (new/low-traffic pages). However, it doesn't explicitly mention when to choose this over sibling tools like 'get_domain_competitors' or 'get_keyword_competitors'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a read operation (implied by 'returns'), includes comprehensive data insights, supports filtering, and discloses the API cost ('1 credit per result row returned'). It also notes the ranking scope for Google vs. Bing. While it doesn't cover all potential behaviors like error handling or pagination details, it provides substantial context beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in three sentences: the core purpose, the data returned, and usage/cost details. Every sentence adds value without redundancy. It's front-loaded with the main function and appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (7 parameters, nested objects) and lack of annotations or output schema, the description does well by covering purpose, data insights, filtering, and cost. However, it doesn't detail the response format or pagination behavior, which would be helpful for an agent to interpret results. It's largely complete but has minor gaps in output expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds some value by mentioning filters ('position range, search volume, difficulty, or keyword patterns') and the API cost implication, but it doesn't provide additional parameter semantics beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns a list of keywords for which the specified URL ranks in top-100 Google search results.' It specifies the verb ('returns'), resource ('keywords'), and scope ('top-100 Google search results'), distinguishing it from sibling tools like get_domain_keywords or get_keywords_info which have different scopes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: analyzing a specific URL's keyword rankings. It mentions 'Use filters to narrow down by position range, search volume, difficulty, or keyword patterns,' which gives practical guidance. However, it does not explicitly state when not to use it or name alternative tools for different use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a read-only analysis tool (implied by 'identifies'), discloses the cost model ('API cost: 1 credit per result row returned'), and explains the meaning of the weight metric in results. However, it doesn't mention rate limits, authentication needs, or pagination behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with four sentences that each earn their place: purpose statement, weight metric explanation, use case context, and cost disclosure. It's front-loaded with the core functionality and contains zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.