Google Search Console MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v2.4.0
Disambiguation3/5
Many tools serve overlapping purposes: 'quick_wins' and 'image_search_quick_wins', 'content_decay' and 'image_content_decay', 'ctr_opportunities' and 'ctr_vs_benchmark', plus multiple alert/drop detection tools. While descriptions differentiate them, the sheer number of similar-sounding tools creates ambiguity for an agent trying to pick the right one.
Naming Consistency5/5
All tool names use a consistent snake_case pattern with descriptive noun pairs (e.g., 'traffic_drops', 'site_snapshot', 'inspect_url'). No mixing of naming conventions (camelCase, PascalCase) is present, making the set predictable.
Tool Count3/5
28 tools is high for a single server but partially justified by Google Search Console's breadth. However, many image-search variants (6 tools) and aggregated reporting tools (e.g., 'generate_report', 'content_recommendations') could be consolidated, making the set feel heavier than necessary.
Completeness4/5
The tool set covers a wide range of SEO workflows: performance overviews, keyword opportunities, CTR optimization, traffic diagnostics, indexing management, image search analytics, and even AI conversation queries. Minor gaps exist (e.g., no tool for managing sites or disavow files), but core analysis and action use cases are well-covered.
Average 3.9/5 across 28 of 28 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 4 community issues answered or closed in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on August 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 describes the tool as a query runner but does not disclose behavioral traits such as idempotency, side effects, rate limits, authentication needs, or error handling. The IMPORTANT section instructs the agent on analysis and presentation, not on the tool's own behavior. The agent cannot assess whether the tool is read-only or has destructive potential.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise for the main functional part, but it includes lengthy IMPORTANT and PRESENTATION sections that are instructions for the agent, not about the tool itself. These sections add clutter and could be reduced or moved to a separate system message. The structure front-loads the core purpose, which is good, but the extra content dilutes focus.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 output schema, no annotations), the description covers the query capabilities well but lacks details on return format, pagination behavior, error handling, and data limits beyond row_limit. The IMPORTANT section provides analysis guidance but not tool context. Overall, the description is adequate but leaves gaps in behavioral and output expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters with descriptions. The description adds minimal new meaning: it lists the supported dimensions and search types, which are also present in the schema. It does not explain parameter interactions or provide examples beyond what the schema offers. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Run a custom search analytics query with flexible dimensions and filters.' It lists supported dimensions and search types, making the purpose specific. However, it does not explicitly differentiate from 28 sibling tools (e.g., traffic_drops, content_gaps), relying on the phrase 'For power users who need specific data cuts' to imply a more advanced role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides indirect guidance ('For power users who need specific data cuts'), suggesting when to use it over simpler predefined reports. However, it does not state when NOT to use it, nor does it name specific alternative tools. The agent must infer that this is a general-purpose query tool versus specialized sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that two GSC API calls are joined on query and an impressions ratio is computed, which gives some behavioral context. However, with no annotations, it lacks transparency on critical aspects like authentication needs, rate limits, error handling, or read-only nature. The analysis instructions do not describe tool behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly long due to two large paragraphs (IMPORTANT and PRESENTATION) that provide user instructions and output formatting rules, which do not describe the tool itself. A concise description would be the first two sentences. This dilutes the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description does not specify what fields are returned (e.g., query, web impressions, image impressions, ratio). The presentation instructions imply a visualization, but the raw data structure is missing. The tool's complexity (two joined API calls) demands more detail about the response format to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with clear descriptions for all three parameters. The tool description adds no additional parameter meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: returning side-by-side web vs image search performance per query, including an impressions ratio. This distinguishes it from sibling tools like 'image_search_quick_wins' or 'image_keyword_overview' by emphasizing the comparison between the two search types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 (e.g., when a direct comparison is needed vs. analyzing each channel separately). The 'IMPORTANT' and 'PRESENTATION' sections focus on analysis rules and output formatting, not on usage context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 does not disclose that the tool is read-only (safe to invoke without side effects), nor does it mention any rate limits, authentication needs, or whether results are cached. The presentation instructions are about output formatting, not 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The functional description is concise (first 3 sentences). However, the lengthy presentation guidelines (5 sentences) add significant bloat that could be a separate instruction. The description earns its place for the core purpose but wastes space on output formatting details that are not about the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters and 25+ siblings, the description lacks completeness. It does not explain what data is returned (no output schema), how the results are structured, whether pagination is supported, or how it differs from similar image tools like image_impressions_no_clicks. The presentation instructions are unnecessary for contextual completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 4 parameters. The description adds no extra meaning beyond the schema (e.g., does not explain how min_impressions relates to 'which pages are surfacing' or what position sorting means in this context). 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states this tool ranks pages by image-search performance, distinguishing it from siblings like image_keyword_overview (which maps ranking queries to pages). The verb 'ranked' and the specific resource 'pages on the site by image-search performance' make the purpose explicit and unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when analyzing which pages surface in Google Images vs. which do not, and pairs it with image_keyword_overview. However, it does not explicitly state when not to use this tool or provide alternatives among the many sibling tools (e.g., image_impressions_no_clicks, image_content_decay) 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure. It only says the tool requests crawling and indexing; it does not mention side effects, rate limits, authentication, error behavior, or how the request outcome is reported. The note about JobPosting/BroadcastEvent is the only additional behavioral trait, but it 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose, but it includes a lengthy IMPORTANT section about data analysis and reporting that is tangential to the tool itself. This adds unnecessary verbosity and distracts from the tool's core behavior, making it less concise than it could be.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema and no annotations, the description covers the main function and one caveat (official schema support). However, it does not explain return values, failure modes, or any expectations after submission, leaving a moderate gap in contextual completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides complete descriptions for both parameters ('url' and 'action') with 100% coverage. The description adds minimal value beyond the schema, such as tying the action to new/updated content, but it does not introduce new syntax or clarifications, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool submits a URL to Google's Indexing API for crawling and indexing, and it distinguishes itself from siblings like submit_sitemap and submit_batch by targeting individual URL submission. It also clarifies the intended use for new or updated content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear context for use: notifying Google of new or updated content. It also includes a note about Google's official schema support versus all page types, which gives practical guidance. However, it does not explicitly mention alternatives or exclusions, so it falls short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly discloses important behaviors: base analysis only on returned data, report exact numbers, do not speculate, say when information is insufficient, and always present results as interactive visualizations with specific design elements. This goes beyond simple API behavior to govern how the agent should interpret and present results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a main purpose statement followed by IMPORTANT and PRESENTATION sections. While this adds structure, the PRESENTATION section is lengthy and prescriptive, containing detailed visual design instructions that could be more concise. It is not tightly packed but still has a clear hierarchy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains what metrics are aggregated and mentions top pages and queries, but it lacks details on how path_pattern matching works (exact match, prefix, regex), data format, or pagination. Since there is no output schema, the description should compensate for these gaps, but it only partially does so. The analysis guidelines are valuable, but the return structure is under-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'days' and 'path_pattern' documented in the schema. The description adds no new parameter semantics; it only mentions 'URL path pattern' which mirrors the schema. Thus the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Aggregates') and clearly states the resource ('all pages matching a URL path pattern') and the output (clicks, impressions, CTR, position, top 5 pages and queries). It distinguishes itself from sibling tools by focusing on group performance rather than individual URLs or specific opportunity types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not state when to use this tool vs alternatives. It gives no context for choosing it over sibling tools like 'site_snapshot' or 'advanced_search_analytics', and no mention of exclusions or prerequisites. The only guidance is about data handling, not tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It does not disclose whether the tool is read-only, what permissions are needed, or if there are rate limits or side effects. It does add behavioral context like 'Filtered to type=image' and a caution against speculation, but lacks transparency on data mutation or authenticity status. Given the absence of annotations, a 3 is appropriate for partial but insufficient disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise and front-loaded with the core purpose. The 'IMPORTANT' and 'PRESENTATION' sections, while lengthy, earn their place by setting behavioral constraints and presentation requirements for the agent. It could be slightly tighter, but most sentences serve a distinct purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not explain the return structure explicitly, but the presentation requirements (summary cards, bar charts, tabbed sections) indirectly specify the intended output. The tool has 4 optional parameters and sibling tools imply complexity. The description is fairly complete for a keyword-oriented analytics tool, though lacking return field details reduces the score from 5 to 4.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description does not need to detail each parameter. However, it adds value by stating that image search is lower volume and that a 90-day default is appropriate, which contextualizes the 'days' parameter meaningfully. This goes beyond schema defaults and enum descriptions, justifying a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'Top image-search keywords for the site, sorted by impressions, clicks, or position' and explicitly distinguishes it from general web search by stating 'Filtered to type=image so it returns only what surfaces in Google Images, not web search.' This verb+resource clarity and sibling differentiation is strong, though it could be slightly more concise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes important presentation and behavioral instructions but lacks explicit guidance on when to use this tool versus alternatives like 'image_keyword_trends' or 'image_pages_overview'. It tells the agent to 'Base your analysis ONLY on the data returned' and not to speculate, but does not clarify prerequisites, ideal scenarios, or when NOT to use it. Context is implied but not systematically differentiated from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It adds the daily quota of 200 URL notifications and the bulk nature, which is helpful. However, it does not disclose return format, error handling, or authentication needs. The IMPORTANT note about analytical behavior is not about the tool's operation, so it adds no transparency value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The first two sentences are concise and purposeful. However, the long IMPORTANT paragraph about analytical behavior is irrelevant to the tool's functionality and should not be in the description. It adds noise and makes the description less focused, so it loses points for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, use case, and quota limit, giving basic context. However, with no output schema and no annotations, it should have mentioned what the API returns (e.g., success counts, errors) or how failures are reported. The extraneous IMPORTANT note does not contribute to completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes both parameters: 'urls' (max 200) and 'action' (enum with explanations). The description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states exactly what the tool does: 'Submit up to 200 URLs to Google's Indexing API in one go.' This uses a specific verb (Submit), resource (URLs/Indexing API), and scope (up to 200, bulk), and clearly distinguishes it from sibling tools like submit_url and submit_sitemap by focusing on batch submission.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use for bulk indexing requests after publishing multiple pages or a site-wide update.' This tells the agent when to use the tool, though it does not explicitly mention alternatives or when not to use it (e.g., for single URL submissions).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral aspects. The description covers presentation intent (visualizations, dashboard-like output) and analysis constraints, but it does not disclose key behavioral traits such as whether the tool performs any mutation (assumed read-only, but not stated), any rate limits, data freshness windows, whether it requires specific authentication scopes, or whether results are cached. The missing disclosure of mutation status and other runtime behaviors is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized, starting with the core purpose, then behavioral rules, then presentation instructions. It is not excessively long, though the presentation section is somewhat verbose with multiple examples. Each sentence adds value except possibly the last few bullet-like examples (e.g., summary cards, bar charts) which could be condensed without losing meaning. Overall, it earns its length for a tool with no annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 required parameters, no output schema, and no annotations, the description does a reasonable job of covering what the tool does and how to use it. It explains the analysis method (two-window join, deltas) and sets clear expectations about output format and analysis boundaries. However, it lacks any mention of return structure (e.g., what fields are returned, pagination), which would be helpful since there is no output schema. Minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage with descriptions for all four parameters (days, order_by, row_limit, min_combined_impressions), so the description is not required to explain them. The baseline of 3 is appropriate because the description adds no additional context beyond what is in the schema. It does not, for example, explain valid ranges for 'days', or clarify that 'order_by' values like 'impressions_delta' are computed deltas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as analyzing 'period-over-period trend for image-search queries' with specific metrics (impressions and position deltas). It explains the mechanics of two equal-length windows joined on query and defines what negative position delta means. This distinguishes it from siblings like image_keyword_overview (likely single-period or snapshot) and image_search_quick_wins (likely opportunity-focused).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides strong guidance on when to use this tool by explaining the period-over-period analysis structure. It also includes explicit usage rules: 'Base your analysis ONLY on the data returned' and 'Do not speculate about causes.' However, it does not explicitly mention when one should use an alternative sibling tool (e.g., image_keyword_overview for static data, or compare_web_vs_image for cross-channel comparison), missing a clear 'when not to use' statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses that the tool returns only the listed metrics and comparison data, and explicitly warns against speculation, implying the tool does not provide causal analysis. It also mandates a visualization format, adding context beyond a simple read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, containing separate IMPORTANT and PRESENTATION sections with detailed agent instructions about analysis and visualization. While the first sentence is concise and informative, the additional directives are more about agent behavior than tool functionality, making it over-specified and not appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the key return metrics and the prior-period comparison. It provides enough information for an agent to understand what the tool does and what to expect, though it lacks explicit edge-case or error-behavior details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the 'days' parameter with 100% description coverage. The tool description adds little direct parameter detail, but the mention of 'comparison to the prior period' indirectly clarifies the period semantics. Baseline of 3 is appropriate because the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'quick overview of how the site is performing', followed by the specific metrics returned (clicks, impressions, CTR, position) and the comparison to the prior period. This distinguishes it from sibling analytics tools that focus on deeper or more granular analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by indicating this is for a 'quick overview' but does not explicitly mention alternatives or provide exclusions. The IMPORTANT and PRESENTATION sections give guidance on interpreting and presenting results, but not on when to choose this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description includes strong behavioral instructions: base analysis only on returned data, report exact numbers, do not speculate on causes, and present as an interactive visualization. With no annotations provided, these instructions carry the full burden of transparency and do so effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but well-structured with 'IMPORTANT' and 'PRESENTATION' sections. Each part conveys necessary guidance, though the presentation instructions are quite detailed and could potentially be tightened without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description explains what the tool returns (queries meeting the stated criteria) and provides analysis guardrails. It doesn't detail exact result fields or limitations, but it is sufficient for an agent to invoke and interpret the tool correctly in most cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides full descriptions for all three parameters (days, min_position, min_impressions) with 100% coverage. The description references the logic ('rank beyond position 20') but does not add new parameter-specific details beyond what the schema already states, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Find topics you should create content for' and specifies the exact criteria (impressions but rank beyond position 20), distinguishing it from siblings like ctr_opportunities or traffic_drops. The verb and resource are specific, and the logic is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case for content gap analysis but does not explicitly contrast it with alternatives like content_recommendations or ctr_opportunities. It provides clear context about the analysis approach but lacks explicit 'use this instead of X' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It provides important behavioral constraints: base analysis only on returned data, report exact numbers, avoid speculation, and present results as an interactive dashboard. These add context beyond the basic 'find pages' function, though it does not explicitly state read-only nature or side effects, the 'Find' verb and context suggest it is safe.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and then clearly separated into IMPORTANT and PRESENTATION sections. It is longer than a minimal one-liner, but each section adds value, especially the behavioral and presentation guidance. The structure makes it scannable, though the presentation instructions could be considered general guidance rather than tool-specific.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should clarify what data is returned. It states it finds pages but does not specify the fields or structure of results (e.g., exact metrics, sorting). It also does not differentiate from the sibling ctr_vs_benchmark, which likely offers similar insights. However, the behavioral guidance about reporting exact numbers gives some hint, and the simple two-parameter configuration keeps the gap moderate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents both parameters (days, min_impressions) with descriptions, so the baseline is 3. The description does not add any extra meaning about how these parameters affect thresholds or results beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: finding pages with high impressions but CTR below expected for their position, identifying them as title/meta description optimization candidates. This is a specific verb+resource+condition and is distinct from siblings like traffic_drops or content_gaps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for identifying optimization candidates, but does not explicitly state when to use it over alternatives like ctr_vs_benchmark or content_recommendations. No explicit exclusions or comparisons with sibling tools are provided, only the implied use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It states the behavioral effect ('Triggers Google to recrawl'), but does not disclose permissions, rate limits, or response format. The IMPORTANT instruction about analysis is more about agent conduct than tool behavior, adding limited transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, with the first two concisely stating purpose and effect. The third sentence is a longer IMPORTANT instruction that adds operational guidance, but is still focused and not redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the action and mentions 'data returned' but does not specify the return value or structure. With no output schema and no annotations, this lack of return-value detail leaves a gap for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the single parameter (sitemap_url) fully, including its default behavior. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Notify Google of a new or updated sitemap. Triggers Google to recrawl the sitemap and discover new pages.' It uses a specific verb (notify) and resource (sitemap), distinguishing it from sibling tools like submit_url or submit_batch.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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: for notifying Google of new or updated sitemaps. It does not explicitly mention alternatives or exclusions, but the purpose is straightforward and the sibling tool list implies other submission methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of behavioral disclosure. It adds important constraints: analyze only returned data, don't speculate, and present results as rich visualizations. This goes well beyond the schema and clearly communicates expected agent behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than average but well-structured with clear purpose first, then IMPORTANT and PRESENTATION sections. The instructions are purposeful and aid correct agent behavior, though the PRESENTATION block could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter analysis tool, the description covers the objective, output specifics, and behavior constraints. There's no output schema, so the description compensates by describing what results include. Minor gap: no mention of data limits or edge cases, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters (days, min_impressions) are fully described in the schema with defaults, so the description adds little beyond that. The mention of 'combined impressions' aligns with min_impressions but doesn't extend the schema's semantic depth.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it finds keywords where multiple pages compete, with specific outputs (rank, position gap, impression split). This distinguishes it from sibling analysis tools like content_gaps or traffic_drops.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for identifying internal competition but provides no explicit guidance on when to choose this tool over alternatives like content_gaps or ctr_opportunities. No mention of when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool makes an additional API call for the timeline, explains data limitations (Google counts follow-ups as new queries, folds AI Mode into web search type), and describes classification into seven buckets with landing pages. However, it mixes in extensive presentational instructions that are not about the tool's own behavior, which slightly detracts from focus.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, containing a large paragraph and additional IMPORTANT/PRESENTATION sections that go beyond tool explanation into agent analysis and output formatting instructions. While the first part is well-structured and front-loaded, the extra sections make it longer than necessary, reducing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description outlines the tool's output (seven buckets with landing pages, monthly timeline) but lacks details on exact return structure, error handling, or rate limits. The IMPORTANT instructions about analysis approach add some contextual guidance for the agent, but the absence of an output schema means the description should compensate more fully. Overall, it is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond what the schema already provides for parameters. It briefly relates the include_timeline parameter to 'one extra API call' but otherwise offers no extra semantics. The schema descriptions themselves are complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool surfaces AI-conversation-related queries from regular query data, classifying them into seven buckets. It distinguishes itself by noting that the dedicated Generative AI report lacks a query dimension, making this the only query-level AI evidence tool. The verb 'surface' and specific resource 'AI-conversation exhaust' provide a precise purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (when query-level AI conversation evidence is needed) and explicitly mentions the Generative AI report as an alternative that cannot provide this data. It also gives caveats like treating probe and harness buckets as machine traffic. However, it does not list exclusions or compare to other sibling tools beyond that one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits like the CTR baseline calibration and the constraint to avoid speculation about causes. Since no annotations are provided, the description carries full burden, and it covers the analysis posture well. However, it does not mention whether the tool mutates data, requires authentication, or has rate limits, leaving some 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement followed by behavioral instructions and presentation guidelines. It is front-loaded with the core function. However, the presentation instructions are somewhat lengthy and could be shortened by referencing a standard output format, but they add significant value for agent usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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), the description provides sufficient context for an agent to use it correctly. It covers what the tool does, how to interpret results, and how to present them. The lack of output schema is partially compensated by the detailed presentation guidance. However, it could mention if the data is aggregated by query or by page for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 does not add additional semantic meaning beyond what the schema provides for the three parameters (days, max_position, min_impressions), as it focuses on the tool's overall purpose and output usage rather than parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds image-search queries ranking at positions 4-15 with high impressions, sorted by estimated traffic gain. It specifies the verb 'find' and the resource 'image-search queries', with explicit ranking and sorting criteria that distinguish it from siblings like 'compare_web_vs_image' or 'image_impressions_no_clicks'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool, including the use of an image-search CTR baseline and instructions to base analysis only on returned data. However, it does not explicitly say when not to use it or name alternatives, leaving some room for ambiguity among the many image-related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It reveals that the tool returns a set of data and includes rules about not speculating beyond the data, but it never states whether the operation is read-only or if there are side effects. The presentation and analysis instructions are helpful but pertain to agent output formatting, not the tool's internal behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a clear first sentence followed by return-field details, analysis rules, and presentation guidelines. While the presentation section is long, it is clearly labeled and provides necessary output formatting context; no sentences are redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description compensates by listing all returned data categories and instructing the agent on how to handle insufficient data. It does not mention potential errors, access requirements, or edge cases, but for a single-parameter read-only inspection tool, the description is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single required parameter 'url' with a clear description ('The full URL to inspect'), and coverage is 100%. The tool description does not add any additional parameter semantics, so it relies on the schema entirely.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Check if a URL is indexed and why or why not', a specific verb+resource that clearly states the tool's function. It then enumerates the output categories (indexing status, crawl date, canonical, robots/noindex, mobile usability), distinguishing it from sibling tools like submit_url or quick_wins.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the tool's use case obvious—diagnosing why a URL is or isn't indexed—but does not explicitly contrast it with alternatives such as submit_url. No exclusion criteria or when-not-to-use guidance is provided, though the purpose is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 return behavior (queries at positions 4-15, high impressions, sorted by traffic opportunity) and adds explicit guardrails for analysis: 'Base your analysis ONLY on the data returned... Do not speculate about causes... If the data does not contain enough information... say so clearly.' This goes beyond a simple statement and helps the agent interpret results responsibly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The main purpose is front-loaded and clear. The additional IMPORTANT and PRESENTATION sections are structured with clear labels and add valuable guidance for an AI agent (avoid speculation, create dashboard-like visualizations). While somewhat long, every section serves a distinct purpose, so the length is justified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description must convey what it returns. It provides a high-level return description (queries with positions and impressions) and gives enough context for selection and invocation. The analysis and presentation instructions further complete the picture, though the exact output structure is not specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for all three parameters (days, max_position, min_impressions), so the schema already provides sufficient semantics. The description mentions 'positions 4-15' which aligns with max_position but adds no new meaning beyond the schema, so a baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Find keywords you're almost ranking for that could be pushed to page one. Returns queries at positions 4-15 with high impressions, sorted by traffic opportunity.' This provides a specific verb (find/returns), resource (keywords/queries), and scope (positions 4-15, high impressions), distinguishing it from siblings like ctr_opportunities or content_gaps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose implies a use case (finding quick ranking wins), but the description does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. There is no reference to sibling tools or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that results are severity-rated, that analysis must be based only on returned data, and that the agent should admit when information is insufficient. These are valuable behavioral traits and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with clear sections (purpose, IMPORTANT, PRESENTATION). It is longer than a simple one-liner, but every sentence serves a purpose: clarifying the read-only nature, limiting speculation, and specifying presentation. This is efficient for an agent-facing tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 optional parameters and no output schema. The description compensates for the missing output schema by specifying how to present results (artifacts, cards, charts, tabs). It also adds essential analysis constraints. It is complete for most usage scenarios, though it could mention error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, fully explaining days, ctr_drop_threshold, click_drop_threshold, and position_drop_threshold. The description does not add parameter-level meaning beyond this, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Check for SEO alerts') and specifies the exact resource: position drops, CTR collapses, click losses, and disappeared pages. This is a specific verb+resource+scope, and it distinguishes from sibling tools like traffic_drops or ctr_opportunities by covering a broader alert set.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool ('Check for SEO alerts') and provides important follow-up instructions on analysis and presentation. However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It provides important behavioral guidance: 'Base your analysis ONLY on the data returned... Do not speculate about causes... If the data does not contain enough information... say so clearly.' This goes beyond simple safety by telling the agent how to interpret results. It also mandates a presentation style, which is an unusual but useful behavioral trait. It does not mention side effects or permissions, but for a read-only comparison tool, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: it starts with the main purpose, then includes cautions about analysis, and ends with presentation instructions. The presentation section is somewhat lengthy but provides concrete direction for the agent's output formatting. Every section serves a distinct purpose, and the structure is easy to parse. Not perfectly concise, but given the amount of behavioral guidance, it earns a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only two parameters, no output schema, and no annotations, the description does a good job of covering what the agent needs to know: what the tool does, how to handle data, and how to present results. However, it does not specify the exact return format or the threshold for 'significantly underperforming,' which is a notable gap. Still, it provides substantial guidance for a tool of this simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes both parameters (days: 'Number of days to analyse', min_impressions: 'Minimum impressions threshold') with 100% coverage. The description adds no additional detail about parameters, so a baseline score of 3 is appropriate. The description's behavioral instructions are helpful but not parameter-specific.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear, specific action: 'Compare your actual CTR per page against industry benchmarks by position.' It also states it 'Flags pages significantly underperforming for their ranking position,' which gives a concrete function that distinguishes it from broader analytics tools. This directly states the tool's purpose with a verb and resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly frames the tool's use case: comparing CTR to benchmarks and identifying underperformers. However, it does not explicitly mention alternative tools or when not to use it. The context is clear enough for an agent to infer appropriate usage, but it lacks explicit exclusions or references to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 that the tool uses three 30-day windows and defaults to a lower minimum click threshold. However, it does not mention any behavioral aspects such as whether the tool is read-only, what happens if data is insufficient, or any rate limits or permissions needed. The transparency is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the key purpose and distinctions. While it includes important usage guidelines later, the overall length is reasonable, though the lengthy list of presentation instructions could be slightly trimmed. Every sentence serves a purpose, making it efficient without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and lack of output schema, the description provides a fairly complete picture: what the tool does, how it differs from siblings, usage instructions, and output presentation format. It could be more complete by briefly mentioning return format or pagination, but it is sufficient for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter with full coverage (100%), so the bar is set at baseline 3. The description mentions a 'lower minimum click threshold' but does not explicitly explain the parameter 'min_period3_clicks' beyond what the schema already states. Thus, it adds marginal value to the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins by explicitly stating this is the 'Image-search version of content_decay,' immediately distinguishing it from the sibling tool 'content_decay.' It then describes the core functionality: analyzing three 30-day windows and flagging pages with consistent decline across all three. This provides a clear, specific verb+resource combination that differentiates from similar tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides detailed usage context, including that the tool defaults to a lower minimum click threshold than the web equivalent due to lower click volumes in image search. It also includes explicit instructions on how to use the results: 'Base your analysis ONLY on the data returned.' and 'If the data does not contain enough information to answer a question, say so clearly rather than guessing.' These guidelines are comprehensive and help the agent decide when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It explicitly instructs the agent to base analysis only on returned data, avoid speculation, and admit insufficient information. This is valuable behavioral context beyond the simple list operation, though it does not detail response pagination or site scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The primary purpose is stated in the first sentence, followed by important usage instructions. The extra sentences are purposeful to prevent hallucinated analysis, but the description is slightly longer than strictly necessary for a simple list tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description provides adequate context about the tool's purpose and output fields. The analysis guidelines further help an agent handle results responsibly. It is sufficiently complete for correct invocation and result interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is trivially complete. The baseline for zero-parameter tools is 4, and the description does not need to add parameter details. Any additional parameter semantics would be irrelevant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('sitemaps submitted for the site'), and clearly lists the returned information (status, errors, warnings, indexed page counts). This clearly distinguishes it from sibling tools like submit_sitemap or quick_wins.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its use for retrieving sitemap submission data, but it does not explicitly mention when to use this tool over alternatives or any exclusions. The added analysis instructions are about interpretation, not about when to invoke the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It goes beyond basic mechanics by mandating strict analysis rules: base conclusions only on returned data, report exact numbers, avoid speculation, and acknowledge insufficient data. It also prescribes rich interactive presentation. Missing are operational details like rate limits or error behavior, but the description is strong on analytical behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Purpose is front-loaded, followed by clearly labeled IMPORTANT and PRESENTATION sections. The presentation section is lengthy but serves a valuable role in guiding agent output. Every sentence provides actionable guidance, though it could be trimmed without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description must define expected results; it does by listing metrics, period comparison, health status, and detailed presentation requirements. It also tells the agent how to handle incomplete data. Operational edge cases are omitted, but for a reporting-oriented tool the description is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters with descriptions (days default, site_urls fallback). The description adds context like 'period comparison' but does not elaborate on syntax, format, or constraints beyond the schema. Baseline 3 is appropriate because schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states a specific purpose: 'Health check across multiple GSC properties in one view' with concrete metrics (clicks, impressions, CTR, position). It distinguishes itself from sibling tools like traffic_drops and content_gaps by focusing on a multi-property dashboard overview rather than a single issue or property.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: for an agency-style multi-property health check with period comparison, which differentiates it from more specialized sibling tools. However, it does not explicitly name alternatives or state when not to use it, so it falls short of full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 clearly states that the tool 'Returns whether the claim is verified and any discrepancy found' and uses 'live GSC data,' giving insight into the expected behavior and output without contradicting any annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose, and every sentence contributes essential information. It is concise without being under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity (six parameters, no output schema), the description sufficiently explains inputs and expected output. It clearly communicates the tool's role as a verification step but could go deeper on edge cases (e.g., handling of invalid data), though this is not necessary for standard use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with descriptions for all six parameters. The description adds little beyond restating the required and optional parameters ('Pass the claim text, the metric to check, the expected value, and optionally a URL or query'). This meets the baseline but does not enrich parameter understanding further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Verify a specific numeric claim against live GSC data.' It uses a specific verb and resource, and distinguishes it from sibling tools by emphasizing verification/self-checking rather than discovery or reporting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for use: 'Use this to self-check your analysis before presenting findings.' It implies when to use the tool but does not explicitly mention alternatives or when not to use it, which would merit a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It adds important behavioral rules: base analysis only on returned data, report exact numbers, avoid speculation about causes, and acknowledge insufficient data. It also mandates a rich presentation style. Yet it does not disclose return structure, rate limits, or read-only status, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than typical but well-structured with a clear definition, measurement rule, important analytical constraints, and presentation guidelines. Every sentence serves a purpose, and it is front-loaded with the main intent, making it efficiently scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of output schema and annotations, the description compensates by defining not only the tool's logic but also how to interpret and present results (e.g., summary cards, color-coded indicators, tabbed sections). It hints at output categories through 'different categories of results' but does not enumerate specific data fields, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so baseline 4 is appropriate. The description adds context about the tool's selection logic (three consecutive 30-day periods) which is not parameter-specific but provides semantic meaning beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: finding pages with consistent traffic decline over three consecutive 30-day periods. It uses a specific verb ('Find') and resource ('pages'), and the decline criterion distinguishes it from siblings like traffic_drops or quick_wins.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage condition: only use when looking for multi-month consistent declines, and explicitly says 'One bad month is noise' to indicate when the tool is not appropriate. However, it does not name alternative sibling tools or explicitly state exclusions beyond the statistical threshold.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explicitly instructs the agent to base analysis only on returned data, report exact numbers, avoid speculation, and acknowledge insufficient data. This is strong behavioral guidance beyond simple function, covering data fidelity and uncertainty handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than two sentences, but each section earns its place: purpose, output types, behavioral constraints, and presentation format. It is well-structured with clear sections, though the presentation details add length. No waste, but slightly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what the tool returns (prioritised actions), behavioral guardrails, and presentation requirements. It lacks detailed return structure, but no output schema exists, and the description provides sufficient context for a tool of this complexity. The explicit presentation instructions add completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters (days, max_recommendations) are already documented. The description does not add additional semantic detail about parameter usage or effects beyond the schema, so 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: 'Get actionable content recommendations by cross-referencing quick wins, content gaps, and cannibalisation data.' It names the specific data sources, distinguishes it from sibling tools like quick_wins or content_gaps, and describes the output categories (pages to update, content to create, pages to consolidate).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by cross-referencing multiple data sources, positioning it as a combined-recommendations tool versus individual sibling tools. Though it doesn't explicitly state when not to use it or name alternatives, the context is clear enough for an agent to infer appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 that the tool analyzes impression-no-click patterns, defaults tuned for image search, and includes instructions on data interpretation (no speculation). However, it does not explicitly state whether the tool is read-only or whether it modifies any data, but the context implies it is analytical (surfacing data). Minor gap: no mention of performance impact 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose. It includes important context and explicit usage instructions in a few sentences. Some phrasing could be slightly condensed (e.g., the note about 'thumbnail not converting' could be one line), but overall it is efficient without being wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool that identifies zero-click patterns, the description covers purpose, usage boundaries, and presentation expectations. It lacks an output schema, but that is not required per the rubric. The description is complete enough for an AI agent to understand what it does and how to act on results, though a note about output structure (e.g., columns returned) would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all four parameters are described in the input schema. The description does not add additional meaning beyond the schema, but clarifies that defaults are tuned for image search. The schema provides sufficient context for parameters like 'days' and 'row_limit.' Baseline 3 is appropriate since the schema does the heavy lifting and the description adds no new param-specific detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states this tool 'Surfaces query and page pairs that earn meaningful image-search impressions but effectively zero clicks,' using a specific verb ('surface') and resource ('query and page pairs'). It distinguishes itself from siblings like 'image_search_quick_wins' and 'ctr_opportunities' by explicitly naming the image-search context and the 'thumbnail is not converting' pattern, making its unique role evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidelines on when to use this tool, emphasizing it is for image search and noting the higher impression volumes. It includes critical usage instructions: 'Base your analysis ONLY on the data returned,' 'Do not speculate about causes,' and guidance on presentation as a dashboard visualization. This clearly differentiates it from siblings by focusing on zero-click patterns in image search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds crucial behavioral guardrails: mandates analysis based solely on returned data, prohibits speculative causes, requires admitting insufficient data, and prescribes a rich, interactive presentation format. These are substantial additions beyond mere tool purpose and significantly shape agent behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized, front-loading the core purpose and then using 'IMPORTANT' and 'PRESENTATION' sections to convey critical usage rules. It is longer than minimal but each section adds actionable value. Slight verbosity in the presentation details prevents a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 is fairly complete: it explains what the tool does, what kind of output to expect (numbers and diagnoses), and how to present results. It does not specify exact output fields, but the guidance to 'report exact numbers' and the diagnostic categories give a sufficient picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully documents the only parameter ('days') with a description and default value. The tool description only refers to 'period' without adding new details or constraints beyond what the schema provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and resource: 'Find pages that lost the most traffic recently.' It further clarifies the comparative and diagnostic nature ('Compares current period vs prior period and diagnoses whether each drop is a ranking loss, CTR collapse, or demand decline'), which clearly distinguishes it from sibling tools like content_decay or ctr_opportunities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: to identify traffic drops and understand their causes. It also provides strong post-invocation guidance (e.g., base analysis only on returned data, report exact numbers). However, it does not explicitly mention alternative tools or exclusion criteria, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and delivers extensively. It includes critical rules like basing analysis only on returned data, citing exact numbers, avoiding speculation unless supported, explicitly saying when data is insufficient, and requiring presentation as interactive visualizations with specific formatting. This goes far beyond a basic summary.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long, but it is well-structured with clear sections for the main action, analysis constraints (IMPORTANT), and presentation requirements (PRESENTATION). Each sentence earns its place, though some of the presentation details could be considered slightly verbose. It is arguably necessary for such a complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (combining multiple report sections), the absence of an output schema, and no annotations, the description provides thorough context. It explains the report's contents, data analysis rules, handling of insufficient data, presentation format, and file-saving behavior. The only minor gap is that the return value is not explicitly stated, but the overall completeness is high.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters (days, output_path, include_sections) are already fully described in the schema with 100% coverage. The tool description adds no additional semantic details about parameter usage or interdependencies, so 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Generate a comprehensive markdown performance report.' It lists the specific sections covered (snapshot, alerts, quick wins, traffic drops, content decay, recommendations), which distinguishes it from sibling tools that focus on individual analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies appropriate usage contexts such as 'weekly reviews or scheduled reporting,' giving a clear sense of when to use the tool. However, it does not explicitly contrast with alternative sibling tools or state when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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