AI Site Scorer

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 compensates by detailing the analysis scope, the wait_for_result behavior (waiting up to 60s, returning job_id if false), and a thorough pricing breakdown including combined payments. It does not mention potential errors or rate limits for anonymous users, but it covers significant behavioral aspects that an agent would need.

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

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

The description is well-structured with the purpose first, followed by what is checked, then the wait_for_result behavior, and finally pricing. The pricing information is verbose but necessary for cost-aware decisions. It is reasonably concise and front-loaded with the key purpose, earning a strong score.

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

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

The tool has no output schema, so the description must explain return values. It mentions 'scores and recommendations' and that wait_for_result=false returns a job_id, which provides a basic understanding. However, the exact structure of the scores is left vague, and with sibling tools like get_report, there is a gap about whether this tool returns a complete report or just a summary. Slightly incomplete but adequate for an analysis submission tool.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters with descriptions. The tool description adds minimal extra meaning: for wait_for_result it specifies 'up to 60s' (timeout context), and for enhance it repeats the combined pricing already present in the schema. This is a case where the description complements but does not significantly expand upon the schema.

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

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

The description clearly states the tool's action ('Submit a URL for AI-readiness analysis') and the specific resource being analyzed. It also enumerates the aspects checked (robots.txt, structured data, page speed, content quality), making the purpose specific. However, it does not explicitly distinguish itself from sibling tools like get_report or enhance_report, though the core action is sufficiently differentiating.

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

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

The description provides helpful context on how to use the tool, such as setting wait_for_result to wait for completion and the timeout of 60 seconds. It also explains pricing conditions for anonymous vs registered users. However, it does not explicitly state when to use this tool versus alternatives like get_report, leaving the usage guidance implied rather than explicit.

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

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

No annotations are provided, so the description carries the full burden. It discloses cost behavior (free with Pro, $0.005 USDC via x402), anonymous/free-tier access, and the output nature (analysis, not modification). This goes beyond the schema but could add more on time expectations or that it does not alter the report.

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

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

Two sentences with front-loaded purpose. The pricing info is relevant and concise. No redundant or filler content; every sentence adds value.

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

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

For a simple one-parameter tool with no output schema, the description explains what it does, what it returns, and cost/access context. It is complete enough for an agent to select and invoke correctly without further guidance.

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

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

Schema coverage is 100% and job_id is clearly documented in the schema. The description adds no additional parameter details beyond 'completed report,' which mirrors the schema. Baseline of 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool runs LLM deep analysis on a completed report and returns detailed AI analysis with priority issues and recommendations. It uses a specific verb ('Run') and resource ('completed report'), distinguishing it from siblings like analyze_url and get_report.

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

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

The description implies usage on a completed report (job_id provided) but does not explicitly compare to alternatives or state when not to use. Sibling tools are not mentioned, so the agent lacks explicit guidance on choosing this over analyze_url or generate_fix_prompt.

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

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

No annotations are provided, so the description must carry the burden. It discloses the output nature ('returns a comprehensive, actionable developer prompt') and cost model, but does not explicitly state side effects or auth requirements. However, the tool's function suggests a read-only generation, and pricing adds transparency.

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

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

The description is two sentences, front-loaded with purpose, and the pricing information is briefly stated without fluff.

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

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

Given the simple single-parameter interface with no output schema, the description adequately covers purpose, output, and cost. It lacks details on failure behavior or prerequisites beyond 'completed report', but remains sufficient for an agent to select and invoke correctly.

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

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

Schema covers job_id fully with description 'The UUID job_id of a completed report.' The tool description does not add additional parameter semantics beyond that, so baseline 3 applies.

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

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

The description uses a specific verb ('Generate') and identifies the resource ('Cursor/IDE-ready prompt to fix AI readiness issues'), clearly distinguishing it from sibling tools like analyze_url or get_report.

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

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

The description implies usage when a report with AI readiness issues exists, but does not explicitly state when to use this over alternatives or mention exclusions. The pricing info is tangential.

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

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

No annotations are present, so the description carries the full burden. The verb 'Fetch' signals a non-mutating read operation, and it discloses return content (scores, check details, recommendations). However, it doesn't specify error behavior or prerequisites beyond completion, which is a minor gap.

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

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

The description is a single, front-loaded sentence that efficiently states the action and return value. No redundant words or filler.

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

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

For a simple one-parameter fetch tool with no output schema, the description adequately covers purpose, return content, and the requirement that the report be previously completed. It could mention error handling or link more explicitly to analyze_url, but it is fundamentally complete.

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

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

Schema coverage is 100% as job_id is fully described with its type and origin ('returned by analyze_url'). The description adds no new parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description uses a specific verb 'Fetch' with a clear resource 'previously completed analysis report' and identifies the key identifier (job ID). It clearly distinguishes from siblings by indicating it retrieves a single completed report rather than creating, enhancing, or generating prompts.

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

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

The description implies usage after analysis completion ('previously completed') and references job ID, which ties to analyze_url. It doesn't explicitly name alternatives or exclusion conditions, but the context is clear enough for an agent to know when retrieval is appropriate.

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

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

No annotations are provided, so the description carries the transparency burden. It discloses the account scope and return fields, but it states '10 most recent' without acknowledging the configurable limit parameter (1-50), and it does not explicitly note that the operation is read-only. This is adequate but has gaps.

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

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

The description is two sentences, front-loaded with the action and resource, and contains no unnecessary words. Every sentence contributes either the scope or the return contents.

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

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

For a simple list tool with one optional parameter and no output schema, the description covers the essential scope and return fields. However, the '10 most recent' phrasing could mislead when limit is set differently, and no output schema exists to clarify the exact response structure, but overall it is sufficiently complete.

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

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

The schema fully describes the only parameter (limit) with a default and range, so schema description coverage is 100%. The description adds no additional parameter semantics beyond mentioning the default count of 10, meriting the baseline score of 3.

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

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

The description uses the specific verb 'List' and clearly identifies the resource as 'recent analyses for the authenticated account,' which distinguishes it from sibling tools like analyze_url (create) and get_report (single report). It also states the return contents (URLs, scores, statuses), fully clarifying its purpose.

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

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

The description implies usage when a user needs a list of recent analyses, but it provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives such as get_report for fetching a single analysis, so the usage context is clear but not differentiated.

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