Server Quality Checklist

Tool Scores

• Behavior3/5

  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 mentions the tool is 'optimized for LLMs' and supports various features like search depth and filtering, which adds useful context. However, it doesn't cover important behavioral aspects such as rate limits, authentication needs, error handling, or what the output looks like (especially since there's no output schema).

  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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines and key features. Every sentence earns its place by adding value without redundancy, making it efficient and well-structured.

  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 complexity (12 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It covers purpose and usage well, but lacks details on output format, error cases, or operational constraints like rate limits. For a tool with rich input schema but no output schema, more behavioral context would be beneficial.

  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 description mentions that the tool 'supports search depth, topic selection, time range filtering, and domain inclusion/exclusion,' which aligns with some parameters in the schema. However, with 100% schema description coverage, the schema already documents all 12 parameters thoroughly. The description adds minimal value beyond what the schema provides, meeting the baseline for high 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 clearly states the tool 'performs a web search using the Tavily Search API, optimized for LLMs,' which specifies the verb (performs web search), resource (Tavily Search API), and target audience (LLMs). It distinguishes itself by mentioning optimization for LLMs, though without sibling tools, full differentiation cannot be assessed.

  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 broad information gathering, recent events, or when you need diverse web sources.' This gives explicit guidance on appropriate use cases. However, it lacks exclusions or alternatives, which would be needed for a perfect score.

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