Server Quality Checklist

Tool Scores

• read_urlA
  3.8/5.0
  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 explicitly states that content is not truncated by default, formats served, automatic JS rendering behavior, and that it returns an honest typed error rather than silently returning partial or fake content. This is highly transparent, though it omits some details like rate limits or redirect handling.

  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 compact and front-loaded with the primary purpose, followed by key behavioral details. Every sentence earns its place, covering formats, rendering, and error handling without unnecessary fluff. Structurally ideal for quick parsing.

  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 complexity (6 parameters, nested objects, no output schema or annotations), the description covers the essential behavioral aspects: return format, default truncation behavior, handled content types, automatic rendering, and error honesty. The schema handles parameter details, and the description provides sufficient operational context, leaving only minor gaps like timeouts or redirects.

  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 covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds some context (e.g., 'Full content by default (no truncation)' aligns with max_length, and SPA shell behavior relates to render), but it does not provide substantial meaning beyond what the schema already offers.

  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 fetches a web page and returns its content as clean, LLM-ready Markdown, which is a specific verb and resource. However, it does not explicitly differentiate from the sibling tool 'read_docs', so it misses the highest mark for sibling distinction.

  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 contexts (e.g., handling HTML, plain text, Markdown, JSON, and automatic JS rendering for SPAs) but provides no explicit guidance on when to use this tool versus 'read_docs' or any exclusions. It gives clear context but no direct alternative comparison.

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

• read_docsA
  4.2/5.0
  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 mentions 'Same JS-rendering, anti-bot, and honest-error behavior as read_url' and describes content stripping, which gives some behavioral insight. However, it relies on the sibling tool's behavior without explaining what those terms mean in this context, leaving gaps about failure modes or side effects.

  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 long, front-loaded with the primary purpose, and every sentence adds value. It efficiently covers what, when, and how this tool differs from the sibling without any fluff.

  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 complexity (5 parameters, nested headers object) and absence of an output schema, the description adequately explains the return format ('clean Markdown') and use case. It references shared behavior with read_url, which is acceptable for an agent familiar with that sibling, but could be more self-contained. Overall complete enough for a motivated agent.

  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 100% of parameters with detailed descriptions, so the baseline is 3. The tool description adds no additional parameter context (e.g., examples or relationships), but it doesn't need to since the schema already handles this dimension.

  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 ('Fetch') and resource ('documentation or reference page'), and explicitly states the output transformation ('return only main content as clean Markdown'). It distinguishes itself from the sibling tool read_url by specifying 'for general web pages use read_url', making the purpose unmistakable.

  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 explicitly states when to use this tool ('for API docs, guides, and reference pages') and when to use the alternative ('For general web pages use read_url'). This gives clear context and directly addresses tool selection.

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