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

No annotations are provided, so the description must carry the full burden. It mentions what data is returned (crawl frequency, error types, problem list) but does not disclose behavioral aspects like authentication needs, rate limits, error handling, or side effects. The verb 'Get' implies read-only, but this is not explicit.

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?

Two sentences with front-loaded purpose and specific details. Zero wasted words, efficiently communicates core functionality.

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 description adequately covers the tool's purpose and key output for a 3-parameter tool with no output schema. However, it lacks details on return format or error conditions. Given the absence of output schema, a bit more on expected results would improve completeness.

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 descriptions (site_url, show_issues, max_issues). The tool description adds context about what the parameters control (e.g., 'specific crawl issues') but does not provide additional meaning beyond what the schema already conveys. Baseline 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 verb 'Get' and resource 'crawl statistics and crawl issues' from Bing Webmaster Tools, listing specific metrics (crawl frequency, error counts by type, crawl problems). It distinctly differentiates from sibling tools like bing_keyword_research or bing_sitemap_list, which focus on other 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 implies the tool is for retrieving crawl health data but provides no explicit when-to-use or when-not-to-use guidance versus siblings. No prerequisites or context are given, leaving the agent to infer from the function name and resource.

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 provided, so description must cover behavioral traits. It states what is returned (volume, related keywords) but omits details like read-only nature, rate limits, or authorization 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?

Two focused sentences; no wasted words. Purpose and key differentiating fact are 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 5 parameters, no output schema, and no annotations, description covers the main purpose and data source. Could mention return format, but tool is simple enough that context is mostly 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?

Schema coverage is 100%, so schema already documents parameters. Description adds no extra meaning beyond schema, so 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?

Description clearly states the tool researches keywords using Bing Webmaster Tools, returns search volume and related keywords, and distinguishes itself from siblings by noting Bing's exclusivity.

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?

Implies usage for Bing keyword data, but lacks explicit guidance on when to use vs alternatives, no when-not or alternative mentions.

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 the return contents (URL counts, indexed counts, errors, last crawl time) but does not mention authentication requirements, rate limits, or side effects. For a list tool, it's adequate but not comprehensive.

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 that is front-loaded with the verb and resource, and efficiently lists included data without extraneous 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 no output schema, the description sufficiently explains return values (URL counts, indexed counts, errors, last crawl time). No additional context needed for this low-complexity 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?

There is one parameter (site_url) with 100% schema description coverage. The tool description does not further elaborate on the parameter beyond what the schema provides. The schema already describes the parameter clearly, including the default behavior. 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 'list', the resource 'sitemaps submitted to Bing Webmaster Tools', and the specific data included (URL counts, indexed counts, errors, last crawl time). It distinguishes from sibling tools like gsc_sitemap_list by specifying Bing.

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 Bing sitemap info but does not explicitly state when to use this tool over alternatives (e.g., gsc_sitemap_list). No exclusions or prerequisites are mentioned.

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 describes the tool as inspection with no behavioral disclosure (e.g., readOnly, impact on Bing, rate limits). The list of returned fields is helpful but does not cover behavioral 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 a single paragraph with no wasted words, efficiently conveying the tool's purpose and return data.

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 key return fields (HTTP status, indexing state, etc.), making it reasonably complete. It could mention error handling or unspecified URL scenarios.

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 coverage is 100% with clear descriptions. The description adds value by noting that site_url uses a config default if omitted, which goes beyond the schema 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 states the tool inspects a URL's indexing and crawl status in Bing Webmaster Tools, listing specific return values. It is distinct from sibling tools like gsc_url_inspection (Google) and bing_crawl_health (aggregate health).

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 to inspect a specific URL's status, but provides no explicit guidance on when not to use it or how it differs from other Bing 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?

No annotations are provided, so the description carries full burden. It discloses that the split is based on brand_terms and returns aggregated metrics and top queries, but lacks details on data source (GSC API), rate limits, data freshness, or error cases.

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?

Two sentences, no wasted words, front-loaded with the tool's core purpose. 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?

Given no output schema, the description partially explains return values but lacks format details (e.g., structure of top queries) and omits prerequisites like GSC access. Adequate but not fully comprehensive for a tool with 5 parameters 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?

Schema coverage is 100% with descriptions for all 5 parameters. The description adds value by explaining the output (aggregated clicks, impressions, CTR, position per segment, plus top queries), which goes beyond the schema's parameter-level docs.

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 ('split') and resource ('search traffic into branded and non-branded query segments') and clearly distinguishes from siblings like gsc_search_performance, which provides overall performance, and gsc_striking_distance, which focuses on near-top pages.

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 brand vs non-brand analysis but does not explicitly state when to use this tool over alternatives or provide exclusion criteria. No guidance on prerequisites or 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, the description must fully disclose behavior. It states the tool returns data and supports filtering/grouping, but omits details like read-only nature, rate limits, aggregation behavior (daily vs. total), pagination, or data latency. These gaps are significant for a data query 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 two sentences long, front-loaded with the action and resource. Every sentence adds value with no fluff. 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 has 9 parameters and no output schema or annotations, the description lacks essential context such as default dimension, behavior when multiple dimensions are combined, row limit impact, and how the date range is sampled (daily vs. aggregated). More detail 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?

Schema coverage is 100%, so baseline is 3. The description adds context about return values and operations (e.g., 'Returns clicks, impressions, CTR, and position'), but this information is not critical for parameter usage and doesn't significantly enhance understanding 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 clearly states the tool queries GSC search performance data and lists return fields (clicks, impressions, CTR, position). It mentions filtering and grouping, which distinguishes it from siblings like gsc_brand_nonbrand or gsc_striking_distance, but lacks explicit 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 does not provide guidance on when to use this tool versus alternatives, such as gsc_traffic_drop or gsc_brand_nonbrand. No prerequisites or typical scenarios are mentioned.

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, the description carries the full burden. It reveals the return fields (status, counts, date) but does not disclose read-only nature, auth needs, or behavior on invalid site_url. The disclosure is adequate for a simple list operation but lacks depth.

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 is front-loaded and concise, with no redundant information. Every word serves the purpose.

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 simplicity (one optional parameter, no output schema), the description provides enough context: it lists sitemaps with status, counts, and date. It doesn't cover prerequisites like site verification, but for a straightforward list operation this is 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?

Schema coverage is 100% and the schema already describes the 'site_url' parameter. The description adds no extra meaning beyond the schema, so baseline 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 uses a specific verb 'list' and resource 'sitemaps submitted to Google Search Console', clearly stating what it does and including fields returned (status, URL counts, last submission date). It distinguishes itself from sibling tools that target Bing or other GSC 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 does not explicitly state when to use this tool over alternatives like bing_sitemap_list or other GSC tools. Usage context is implied by the name and description, but no when-not or alternative guidance 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, the description explains the filtering logic (positions 4-20, sorted by impressions descending) and the low-hanging fruit concept. It does not mention destructive actions (none expected) or authentication details, but is generally transparent.

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?

Two focused sentences with no wasted words. The main action and value proposition are 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?

No output schema is provided, and the description does not specify the fields returned (e.g., query, impressions, position). Given the complexity (7 params), an agent might need to infer output structure, leaving a completeness gap.

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 coverage is 100% with descriptions for all 7 parameters, so baseline is 3. The description reinforces the default position range but adds no new information 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 clearly specifies the verb 'find' and resource 'queries ranking in positions 4–20', and the term 'striking distance / low-hanging fruit' uniquely identifies the tool's niche among siblings like gsc_search_performance.

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 states these queries are 'best candidates for quick ranking improvements', implying when to use. However, it does not explicitly exclude alternatives or provide when-not-to-use 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 provided, so the description carries the burden. It only states 'identify pages or queries with significant traffic drops' without detailing the comparison mechanism, return format, or potential side effects. The parameter descriptions in the schema partially compensate.

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 with no unnecessary words, front-loading the purpose and secondary sentence providing context. It is 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?

No output schema exists, and the description does not explain what the return format is or the exact metrics compared. It mentions 'traffic drops' but doesn't specify if it's clicks, impressions, etc. The parameter details help fill the gap, but more context would improve completeness.

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 all parameters are documented in the schema. The description adds no additional meaning beyond what is in the schema, meeting the baseline for good 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 compares two date periods and identifies pages or queries with significant traffic drops, using verbs 'compare' and 'identify'. It distinguishes from sibling tools like gsc_search_performance by focusing on drops.

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 use cases: 'diagnosing algorithm updates, technical issues, or content decay', giving context for when to use it. It does not explicitly state when not to use it or mention alternatives, but the use cases are relevant.

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, the description carries the full burden. It discloses the specific return fields (crawl date, indexing verdict, canonical URL, rich results eligibility, mobile usability status), which are behavioral traits beyond the input schema. However, it does not mention idempotency, rate limits, or auth requirements, which are minor 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 action and resource. Every clause adds value (listing return fields), 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 no output schema, the description compensates by enumerating return fields. It covers the main purpose and outputs. Missing are error conditions or prerequisites (e.g., required GSC access), but for a simple inspection tool, this is adequate.

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 already has 100% coverage with descriptions for both parameters ('url' and 'site_url'). The tool description adds no additional parameter meaning beyond the schema, earning the baseline score of 3.

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 tool as inspecting a URL's indexing status in Google Search Console, with a specific verb ('Inspect') and resource. It lists key outputs (crawl date, indexing verdict, canonical URL, rich results eligibility, mobile usability), which distinguishes it from sibling tools like bing_url_inspection (for Bing) and other GSC tools (e.g., gsc_search_performance for aggregated 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 implies the tool is used for inspecting individual URLs' indexing status, but it does not explicitly state when to use it (e.g., for debugging indexing issues) versus alternatives (e.g., bulk inspection not available). No exclusion criteria or context about prerequisites (e.g., property access) is provided.

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