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

With readOnlyHint=true and no destructive hint, the safety profile is covered by annotations. The description adds that the metrics are per-engine and per-location, which is useful context, but it does not disclose rate limits, pagination, or required authentication. This is adequate but not rich.

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, front-loaded sentence that efficiently conveys the core purpose. There is no redundant wording or filler, exactly as expected.

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 lack of an output schema, missing parameter semantics, and only sparse annotations, the description is too minimal. It does not cover usage context, parameter meaning, or return behavior, leaving the agent with an incomplete picture 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?

Schema description coverage is 0%, and the description provides no explanation of query_id or measurement_window_id. The phrase 'single tracked prompt' hints that query_id identifies the prompt, but measurement_window_id is entirely unexplained, leaving a significant gap.

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 returns visibility metrics, further specifying 'per-engine and per-location' and 'single tracked prompt'. This distinguishes it from sibling tools like get_visibility_overview and list_competitor_visibility, even without naming 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?

No guidance is given on when to use this tool versus alternatives. The description only states what it does, not the context or exclusions, so the agent has no basis to choose it over similar 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?

Annotations declare readOnlyHint=true, so the read-only nature is already known. The description adds context about the metric dimensions (brand, engine, measurement window) but does not disclose return format, filtering behavior, or consequences of omitting optional 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?

A single sentence that front-loads the core purpose. No filler or redundancy; every word adds meaning.

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?

With no output schema, the description doesn't explain what the returned metrics look like or how the optional parameters affect results. For a low-complexity tool with two parameters, this is minimally sufficient but leaves gaps around result structure and use cases.

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 0%, so the description must compensate. It maps brand_id and measurement_window_id to 'per brand' and 'measurement window', but provides no detail on optionality, expected formats, or how the output varies when parameters are omitted.

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 uses a specific verb ('get') and resource ('visibility overview'), and clarifies scope as 'per brand and engine for a measurement window.' This distinguishes it from siblings like get_workspace_overview or get_prompt_visibility, 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?

The description implies usage when brand/engine-level visibility metrics are needed, but gives no explicit guidance on when to prefer this tool over siblings like get_workspace_overview or list_competitor_visibility. No exclusions or alternatives are 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?

The annotations already declare readOnlyHint=true, which covers safety expectations. The description adds context about the scope of the overview (what data is included), but does not disclose additional behavioral traits such as return format or potential limitations.

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, concise sentence that directly enumerates the tool's output components with no filler or repetition. Every word adds value, making it appropriately sized and 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?

Given no output schema, the description lists the key return components (name, brand, counts, competitors, latest window), which covers the essentials for an overview. However, it does not elaborate on the structure or types of these fields, but for a no-param read-only overview, this is reasonably 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?

With zero parameters and 100% schema coverage (empty object), there is no parameter information needed. The baseline for no-parameter tools is 4, and the description adds no irrelevant parameter details.

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 lists the resource and its contents (workspace name, target brand, counts, competitors, latest measurement window), and the name 'get_workspace_overview' implies a retrieval action. It distinguishes itself from sibling list tools by presenting a composite overview rather than a singular list.

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?

There is no explicit guidance on when to use this tool versus alternatives like list_prompts or get_visibility_overview. The 'overview' term implies a high-level summary use case, but no clear context or exclusions are 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?

The readOnlyHint annotation already covers the non-mutating nature. The description adds no further behavioral details such as pagination, ordering, or authentication requirements. It does not contradict the annotation, but it doesn't add beyond it.

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, concise sentence that communicates the core purpose efficiently. It is front-loaded and free of unnecessary detail.

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?

With no output schema and no parameter descriptions, the description must compensate, but it only describes the general output shape. It omits how parameters affect results, how grouping works, or any return format details, leaving the agent under-informed for a tool with 3 optional 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 0% and the description does not mention any of the parameters (limit, domain, measurement_window_id). The agent receives no guidance on parameter meaning, which is a significant gap given all parameters are optional and their semantics are not self-evident from names alone.

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 identifies the resource (domains cited in AI answers) and the output (grouped counts and sample URLs), which distinguishes it from sibling tools. It lacks an explicit verb but the tool name implies listing, and the description is specific enough to avoid confusion.

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 a use case (inspecting cited domains) but provides no explicit guidance on when to use this tool versus alternatives like list_prompts or get_visibility_overview. No exclusions or alternative mentions are present.

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?

The annotations already indicate readOnlyHint=true and openWorldHint=false, so the description does not need to restate safety. It adds the scope of what is compared (target brand and tracked competitors), but it does not disclose details like return format, default behavior without a measurement_window_id, or whether it returns data per competitor. This provides limited behavioral clarity beyond the 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 a single, front-loaded sentence that is concise and free of filler. It efficiently states the action and the subject matter, 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?

With no output schema and a single undocumented parameter, the description is too minimal to fully equip an agent. It explains what the tool does at a high level but omits details about the expected input and the shape of the output, which are necessary for correct invocation and interpretation. The sibling tools provide context, but the description itself does not.

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 has one parameter, measurement_window_id, with no description and zero schema_description_coverage. The tool description does not mention this parameter at all, leaving its purpose and possible values entirely undefined. Since the coverage is 0%, the description fails to compensate for the missing parameter information.

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 uses the specific verb 'Compare' and identifies the resource as 'AI visibility across the target brand and tracked competitors.' This clearly distinguishes it from sibling tools like get_visibility_overview, which likely focuses on a single entity, and list_prompts, which lists prompts. The tool's core function is unambiguous.

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 by stating its purpose, but it does not provide explicit guidance on when to choose this tool over alternatives like get_visibility_overview or get_prompt_visibility. There is no mention of exclusions or prerequisites, so the agent must infer from the tool name and 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?

The description does not contradict the readOnlyHint=true annotation and adds minor context (daily granularity, specific crawlers). However, it does not disclose behaviors like pagination, ordering, or how 'tracked sites' are determined. With annotations covering the read-only nature, a score of 3 is appropriate.

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, front-loaded sentence that immediately conveys the core subject. It is appropriately concise with no wasted words, and the parenthetical list of crawler names adds useful specificity without bloat.

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 list tool with optional filters and no output schema, the description is minimal. It omits any indication that the tool supports filtering, what the response format looks like, or how to interpret the data. The parameter semantics gap makes the tool incomplete for an agent to use 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 0%, and the description does not explain any of the three parameters (limit, crawler_id, site_key_id). The parameter names are somewhat self-explanatory, but the description fails to connect them to the data, leaving the agent without crucial filtering information.

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 identifies the resource (AI crawler visits) and indicates the type of data returned, but it relies on the tool name for the verb 'list'. It distinguishes from siblings by topic (crawler events vs. visibility, prompts, etc.), and the specific crawler names add clarity.

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 on when to use this tool versus alternatives. It does not mention exclusions or specific use cases, and there is no comparison to sibling tools like get_visibility_overview or list_citations. The agent is left to infer its purpose from the name and data 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?

Annotations declare readOnlyHint=true, covering the safety profile. The description adds ordering behavior ('newest first') and a definitional clarification ('tracking snapshots'), but does not disclose other behavioral details like pagination or default limits. With annotations present, this is acceptable baseline.

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, correctly front-loaded sentence that conveys the core purpose and key ordering detail 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 simple list tool with readOnlyHint and two optional parameters, the description is minimally adequate. However, it does not explain what data is returned (no output schema) or the semantics of 'status' and 'limit', leaving notable gaps for an agent to make informed invocations.

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 0%, and the description provides no information about the 'limit' or 'status' parameters. The agent must infer their meaning from names alone, with no guidance on allowed values or how they affect results. The description does not compensate for the lack of 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 uses a specific verb 'List' and identifies the exact resource 'measurement windows' with a clarifying parenthetical 'tracking snapshots' and ordering 'newest first'. This clearly distinguishes it from sibling tools that list prompts, citations, recommendations, etc.

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 this is the tool for listing measurement windows, which provides clear context for when to use it. However, it does not explicitly mention alternatives or when not to use it, but the resource name is distinct enough 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?

Annotations already declare readOnlyHint=true, and the description is consistent with that. It adds valuable context by specifying the scope ('tracked') and the output fields, which tells the agent what to expect. However, it does not mention pagination or response limits despite the presence of a limit parameter.

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 sentence, front-loaded with the verb, and contains no redundant or unnecessary words. It efficiently communicates the tool's purpose and key output attributes.

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?

The core operation is clear and the output fields are listed, but the 3 optional parameters are left unexplained. While the parameter names are relatively intuitive and the tool is simple, the 0% schema coverage and lack of any parameter semantics create notable gaps 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?

Schema description coverage is 0%, and the description does not explain any of the three parameters (limit, active, search). The phrase 'active state' hints at the 'active' filter, but 'limit' and 'search' are entirely undocumented, so the description fails to compensate for the lack of 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 uses the specific verb 'List' with the resource 'tracked prompts' and enumerates the returned fields (text, intent, tier, active state). This clearly distinguishes it from sibling list tools like list_citations or list_recommendations, which target different entities.

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 the tool is used for listing prompts, but it does not explicitly state when to use it versus alternatives like get_prompt_visibility or other list tools. No prerequisites or exclusions are mentioned, so the guidance remains at the level of implication.

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?

The annotation readOnlyHint=true already covers the safety profile, and the description adds ordering behavior and content type. However, it doesn't disclose how parameters like limit, status, or category affect results, nor what the response structure looks like beyond the ordering.

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, tight sentence with no extraneous wording. It efficiently states the core purpose and ordering, though its brevity borders on under-specification rather than conciseness.

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 three parameters, no output schema, and minimal annotations, the description is incomplete. It provides a high-level purpose but lacks detail about parameter semantics, return format, or how this tool fits among the sibling tools, making it insufficient for reliable invocation 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 0% and the description provides no information about the three parameters (limit, status, category). With the description failing to compensate for the complete lack of parameter documentation, the agent has no basis beyond parameter names to infer their expected values or behavior.

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 identifies the tool as returning AI visibility optimization recommendations ordered by priority, making the resource and scoping clear. However, it lacks an explicit verb like 'list' or 'get,' relying on the tool name for that, and it doesn't explicitly distinguish from sibling tools such as get_visibility_overview.

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?

There is no guidance on when to use this tool versus alternatives. The description only states the output ordering ('highest priority first') and gives no contextual cues about prerequisites, filtering, or whether other tools should be preferred for specific scenarios.

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