Server Quality Checklist

Tool Scores

• readability_score_textA
  4.4/5.0
  Behavior5/5

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

  The description discloses important behavior beyond the read-only/idempotent annotations: non-Latin text yields supported=false, metric values can be null, only structural counts are returned for unsupported input, and empty or over-long text raises an error. It also documents the JSON return shape in detail, which matters because no output schema is present.

  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 front-loaded with a precise purpose and then organized into useful sections: usage, args, returns, examples, and errors. The Args section duplicates schema details, but the rest of the content earns its place because it covers usage scenarios, fallback behavior, and failure conditions.

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

  Completeness5/5

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

  For a small, low-risk tool with no output schema, the description is unusually complete: it covers supported inputs, output metrics with units and null semantics, error conditions, and common use cases. There is no crucial operational gap for an agent deciding whether to call and how to interpret results.

  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 already documents both parameters and their defaults. The description mostly restates the parameter constraints and adds useful-but-not-critical context such as splitting long documents; it does not meaningfully increase parameter-level understanding beyond the schema.

  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 opening sentence names a specific operation—compute Flesch Reading Ease and Flesch-Kincaid Grade Level for a block of English text—rather than restating the tool title. The motivating examples ('check a draft reply', 'which draft reads more simply') make its job and distinction from a simplification tool clear.

  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 gives concrete 'use when' scenarios and an explicit 'don't use when' condition for non-English text, which is strong guidance for tool selection. It does not explicitly point to readability_simplify_text as the alternative, but the separation of scoring vs. simplifying is largely implied.

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

• readability_simplify_textA
  4.7/5.0
  Behavior5/5

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

  Beyond the annotations declaring it read-only and idempotent, the description discloses important behaviors: non-Latin scripts return supported=false with unchanged text, only the first 50 changes are listed when changeCount exceeds 50, and Flesch scores are reported before and after. It also explains error conditions for empty or overly long input.

  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 well-structured and front-loaded: the core behavior appears in the first sentence, followed by usage guidance, argument details, return structure, and error handling. Each section serves a clear purpose and the length is justified by the richness of the behavior being described.

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

  Completeness5/5

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

  With no output schema present, the description compensates by listing the full JSON return shape, including edge-case fields like truncated and supported. It also covers input constraints, non-English behavior, and concrete usage examples, leaving an agent well-equipped to format inputs and interpret outputs correctly.

  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 schema already covers both parameters 100%, but the description adds value by documenting the JSON response structure, field semantics, and the default response_format. It also gives context for the text parameter through examples like passing a draft reply.

  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 states a specific action and resource: rewriting English text in plainer language, breaking long sentences, and substituting bureaucratic vocabulary. It also mentions the Flesch Reading Ease output, which helps distinguish it from the sibling scoring tool by emphasizing that the core job is simplification, not mere scoring.

  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 gives explicit use cases, such as simplifying a response, document, or notice, and also works as a self-check for the agent's own drafted output. It gives a clear when-not-to-use case for non-Latin script, though it does not explicitly name the sibling readability_score_text as the alternative when only a score is needed.

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