Server Quality Checklist

Tool Scores

• route_taskB
  3.2/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It mentions intelligent routing and analysis, but doesn't describe what happens during routing (e.g., does it execute the task, return a recommendation, or proxy to the LLM?), potential limitations, error conditions, or performance characteristics. The behavioral aspects are underspecified.

  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 appropriately sized with 4 sentences: purpose statement, elaboration, and parameter explanations. It's front-loaded with the core functionality. The parameter section could be more integrated, but overall it's efficient with minimal waste.

  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 3 parameters with 0% schema coverage, no annotations, but an output schema exists, the description provides basic parameter semantics and purpose. However, for a routing tool with intelligent analysis, it lacks details about the routing logic, return format (though output schema helps), error handling, and how it differs from siblings. It's minimally adequate but has clear gaps.

  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 0%, so the schema provides no parameter documentation. The description adds basic meaning for all 3 parameters (prompt as task description, files as context, preferences as routing preferences), but doesn't specify format, constraints, or examples. This provides marginal value beyond the bare schema.

  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 purpose: 'Intelligently route a task to the best LLM' and explains it analyzes task characteristics to select appropriate LLM services. It specifies the action (route) and resource (task to LLM), but doesn't explicitly differentiate from sibling tools like 'analyze_parallel' or 'list_services'.

  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 context by mentioning task characteristics analysis and LLM selection criteria (Gemini for large codebases, Qwen for code review), but doesn't explicitly state when to use this tool versus the sibling tools. No clear alternatives or exclusions are provided.

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

• list_servicesA
  3.8/5.0
  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 effectively describes the tool's read-only nature by stating it 'Returns status information' and details the specific data returned (health, type, routing rules), which helps the agent understand the output. However, it lacks information on permissions, rate limits, or error 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 front-loaded with the core purpose in the first sentence, followed by a bulleted list of return details that are directly relevant. Every sentence earns its place, with no redundant or verbose language, making it efficient and easy to parse.

  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 (0 parameters, no annotations, but with an output schema), the description is complete enough for a health-check tool. It explains what the tool does and what information it returns, though it could benefit from more behavioral context like error scenarios or usage timing relative to siblings.

  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 0 parameters, and the input schema has 100% description coverage (though empty). The description appropriately adds no parameter details, as none are needed, and focuses on the tool's purpose and output. This meets the baseline of 4 for zero-parameter tools.

  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 specific action ('Check health and availability') and resource ('all configured LLM services'), distinguishing it from sibling tools like 'analyze_parallel' and 'route_task' which have different purposes. It provides a comprehensive overview of what the tool does beyond just listing services.

  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 implies usage for monitoring service health but provides no explicit guidance on when to use this tool versus alternatives like 'analyze_parallel' or 'route_task'. There are no prerequisites, exclusions, or comparisons mentioned, leaving the agent to infer context.

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

• analyze_parallelA
  4.2/5.0
  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. It discloses key behavioral traits: parallel processing across multiple LLMs for faster analysis, ideal for large codebases. However, it lacks details on error handling, rate limits, authentication needs, or what specific analysis is performed beyond 'analyze'.

  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 key details and parameter explanations. Every sentence adds 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.

  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 (parallel processing with multiple parameters) and no annotations, the description is fairly complete: it explains purpose, usage context, and parameters. Since an output schema exists, it need not detail return values. However, it could better address behavioral aspects like error handling or limitations.

  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 0%, so the description must compensate. It adds meaningful context for all parameters: 'directory' as the path to analyze, 'prompt' as the analysis query, and 'num_workers' as parallel workers with a default. This goes beyond the bare schema, though it could elaborate on parameter constraints or formats.

  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 with specific verbs ('analyze', 'distributes') and resources ('large codebase', 'files across multiple LLM services'). It distinguishes from potential siblings by emphasizing parallel processing for speed, which neither 'list_services' nor 'route_task' would imply.

  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 this tool ('ideal for analyzing large codebases with 20+ files'), but does not explicitly state when not to use it or mention alternatives. It implies usage for speed and scale without naming specific sibling tools as alternatives.

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