Server Quality Checklist

Tool Scores

• characterize_functionC
  2.7/5.0
  Behavior2/5

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

  No annotations are provided, so the description must bear the full burden. It mentions the tool works for 'pure functions' and returns pytest code, but it does not disclose side effects (e.g., file output via output_path), permissions needed, or details about how 'allow_impure' affects behavior. The description is insufficient for understanding the tool's impact.

  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 concise with two sentences that convey the core purpose. However, it could improve structure by briefly listing key parameters or use cases. It is not overly verbose, but it sacrifices completeness for brevity.

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

  Completeness1/5

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

  Given 7 parameters, 0% schema coverage, no output schema, and no annotations, the description is severely lacking. It does not explain parameter meanings, return value structure, or behavioral considerations like file creation. The agent would struggle to use this tool correctly without additional information.

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

  Parameters1/5

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

  Schema description coverage is 0%, and the description does not explain any of the 7 parameters (file_path, function_name, max_cases, etc.). The agent must rely solely on the parameter names, which is inadequate for correct usage. The description adds no value to parameter understanding.

  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: 'Generate characterization (regression) pytest tests for a single Python pure function.' It specifies the verb (generate), resource (characterization tests), and scope (single function), distinguishing it from siblings like characterize_method and characterize_module.

  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 does not provide guidance on when to use this tool versus alternatives (e.g., characterize_method, characterize_module). It implies it's for pure functions but lacks explicit context or exclusions, leaving the agent to infer usage without clear direction.

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

• characterize_methodA
  3.6/5.0
  Behavior2/5

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

  No annotations are provided, so the description bears full burden. It lacks disclosure of side effects (e.g., does writing to output_path happen?), permissions, or rate limits. The return type is mentioned but behavioral traits beyond 'returns' are absent.

  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 concise with a single sentence that covers purpose and return type. The 'v2' prefix adds minor noise but doesn't detract significantly. It could be restructured to front-load key info, but it's already brief.

  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?

  Given 9 parameters and no output schema, the description is too brief. It explains the returned type but not parameter behavior, side effects, or usage constraints, leaving the agent with insufficient context for correct invocation.

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

  Parameters2/5

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

  With schema description coverage at 0%, the description must compensate but does not. It only mentions the return type and that it uses fixture-based setup, leaving parameters like max_cases, allow_impure, and coverage_threshold without explanation beyond their names.

  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 generates characterization tests for a single Python class method, distinguishing it from sibling tools like characterize_function and characterize_module by specifying 'class method'.

  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 implies usage for class methods only, effectively differentiating from siblings which target functions or modules. It is explicit enough to guide an AI agent to choose this tool over others.

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

• characterize_moduleA
  4/5.0
  Behavior4/5

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

  No annotations provided; the description discloses return format (list of CharacterizationResult or error dict) and error handling behavior (non-aborting), adding useful behavioral context beyond the tool name.

  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?

  Two short sentences front-loading purpose and key behavior; no unnecessary words.

  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?

  Explains purpose and return format, but for a tool with 7 parameters and no output schema, the description lacks parameter details and output structure, making it incomplete for correct usage.

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

  Parameters2/5

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

  With 0% schema description coverage, the description adds no explanation for any of the 7 parameters, leaving the agent to infer their meanings from names alone.

  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 it generates characterization tests for every pure top-level function in a module, distinguishing it from sibling tools like characterize_function (single function) and characterize_method (methods).

  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 implies usage for testing all pure top-level functions at once, and mentions that failures don't abort the call, but does not explicitly contrast with alternatives or provide when-not conditions.

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

• health_checkA
  4.5/5.0
  Behavior4/5

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

  No annotations were provided, so the description carries full burden. It explains the two behavioral modes (no network vs. network ping) and their triggers. It lacks details on error handling or permissions, but for a health check tool, the key behavior is 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?

  The description is concise with two sentences and a clear explanation of the probe parameter. Every sentence adds value, and it is front-loaded with the core purpose.

  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 low complexity (one parameter, no output schema), the description covers input behavior and the two modes. However, it does not describe what the tool returns (e.g., status summary), which would improve completeness.

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

  Parameters5/5

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

  Schema coverage is 0%, but the description fully explains the 'probe' parameter: default value and the two behaviors (env/config only when False, LLM ping when True). This adds significant meaning beyond the schema, which only provides type and default.

  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: 'Check Pinion configuration and LLM provider reachability.' It uses a specific verb ('check') and names the resources ('configuration and reachability'), distinguishing it from sibling tools which characterize functions, methods, and modules.

  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 explains when to use each mode of the tool: when 'probe' is False, it only inspects environment variables and config with no network call; when True, it sends an LLM ping. This provides clear context for parameter usage, though it does not explicitly compare to sibling tools or state when not to use this tool.

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