Server Quality Checklist

Tool Scores

• fetch_urlC
  2.9/5.0
  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 of behavioral disclosure. It doesn't mention whether this follows redirects, the response format, size limits, authentication handling, or what happens with malformed URLs. For a network-fetching tool, these behavioral details are important for an agent to use it safely and effectively.

  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?

  A single, concise sentence that communicates the core purpose without waste. Every word earns its place, and the content-type enumeration adds useful specificity.

  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 no annotations, no output schema, and no behavioral guidance, the description leaves significant gaps. An agent cannot determine response format, error handling, rate limits, or how this differs from scrape_url. The tool has moderate complexity (optional headers, nested objects) but the description only addresses the surface-level purpose.

  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 (url and headers) are already documented in the schema. The description adds no additional parametric detail beyond what the schema provides. The baseline of 3 is appropriate when the schema handles parameter documentation adequately.

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

  Purpose4/5

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

  The description clearly states 'Fetch data from a URL' with a specific verb (fetch) and resource (URL), and lists supported content types (API endpoints, JSON, web content). It's clear what the tool does but doesn't explicitly distinguish it from the sibling tool scrape_url, which likely fetches and parses HTML pages.

  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 guidance on when to use this tool versus scrape_url. The description lists supported content types but doesn't explain the difference between fetching raw data and scraping, nor does it mention any exclusions or edge cases. An agent wouldn't know which tool to pick when encountering HTML pages vs API endpoints.

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

• scrape_urlB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, so the description bears the full burden of behavioral disclosure. It doesn't mention whether this is a safe read-only operation, whether it makes network requests, what happens with JS-heavy pages requiring rendering, or any restrictions/rate limits. The 'readable text' wording hints at HTML stripping but no side-effect expectations are disclosed.

  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?

  A single sentence, zero waste, effectively front-loaded. Every word earns its place and there's nothing extraneous.

  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 tool has 5 params including a nested csr object with 4 sub-options, signaling meaningful complexity around rendering modes. No output schema exists, so return format is undocumented. Given the complexity of renderMode/csr options and the sibling fetch_url ambiguity, the description is adequate but could benefit from explaining rendering fallback behavior and what the extracted output looks like.

  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%, so the schema parameter descriptions carry the load. The description adds the 'readable text content' semantic — implying extraction/processing beyond raw fetch. However, it doesn't explain how selector, renderMode, or csr options interact with the extraction logic, though those are documented in the schema. Baseline 3 is appropriate given high schema coverage.

  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 states a clear verb+resource+outcome: 'Scrape and extract readable text content from a web page.' It clearly differs from the sibling tool fetch_url (which presumably fetches raw HTML) by specifying 'readable text content' as the extraction target, giving implicit differentiation.

  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 says what it does but doesn't explicitly say when to use it vs fetch_url, nor when NOT to use it. The presence of a sibling tool named fetch_url suggests a decision point, but the description doesn't address it directly. The purpose wording ('readable text') implies a distinction but doesn't make it explicit.

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