Server Quality Checklist

Tool Scores

• transcribe_speechB
  3.4/5.0
  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 adds useful context about language support (multiple languages with auto-detection) and return values (transcription text and detected language), which goes beyond basic function. However, it doesn't cover important behavioral aspects like rate limits, authentication requirements, error conditions, or processing time expectations.

  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 perfectly concise with three sentences that each earn their place. The first states the core function, the second adds important capability context (language support), and the third specifies return values. No wasted words, and information is appropriately front-loaded.

  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 that there's an output schema (which handles return value documentation) and 100% schema description coverage, the description provides adequate context. It covers the core function, language capabilities, and return structure. However, for a tool with no annotations, it could better address behavioral aspects like performance characteristics or error handling.

  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 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions language detection generally but doesn't provide additional context about the 'language' parameter or 'audio_base64' requirements beyond what the schema already states.

  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 with a specific verb ('Transcribe') and resource ('speech audio into text'). It distinguishes from siblings like 'assess_pronunciation' (which evaluates speech quality) and 'synthesize_speech' (which generates speech), but doesn't explicitly contrast with 'list_voices' (which lists available voices). The purpose is clear but sibling differentiation could be more explicit.

  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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'transcribe_speech' over 'assess_pronunciation' for speech analysis, or when transcription is appropriate versus other audio processing tools. There's only implied usage through the description's functional statement.

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

• list_voicesA
  3.5/5.0
  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 discloses that the tool returns voice IDs, names, languages, and sample previews, which adds useful context about output behavior. However, it lacks details on rate limits, authentication needs, or pagination, leaving gaps for a tool with no annotation support.

  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 two sentences, front-loaded with the core purpose and followed by return details. Every sentence adds value: the first defines the action, and the second specifies output content. There's no wasted text, 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 low complexity (0 parameters) and the presence of an output schema, the description is reasonably complete. It covers the purpose and output semantics, which is sufficient for a listing tool. However, without annotations, it could benefit from more behavioral context like rate limits or auth requirements.

  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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate. Baseline is 4 for 0 parameters, as it doesn't need to compensate for any schema gaps.

  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: 'List all available text-to-speech voices.' It specifies the verb ('List') and resource ('text-to-speech voices'), making the action and target explicit. However, it doesn't differentiate from siblings like 'assess_pronunciation' or 'synthesize_speech' beyond implying it's a listing operation versus synthesis/assessment tools.

  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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing voice IDs for synthesis, or contrast it with sibling tools like 'synthesize_speech' for generating speech. Usage is implied by the listing action, but no explicit context or exclusions are stated.

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

• assess_pronunciationA
  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 carries the full burden of behavioral disclosure. It describes the output (scores and feedback) but does not cover critical behavioral aspects such as performance characteristics (e.g., latency, rate limits), error handling, or authentication requirements. This leaves gaps in understanding how the tool behaves in practice beyond its basic function.

  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 concise summary of return values. Both sentences earn their place by providing essential information without redundancy, making it efficient and well-structured for quick understanding.

  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 that an output schema exists, the description does not need to detail return values, and it adequately covers the tool's function. However, with no annotations and a mutation-like operation (assessment implies processing), it could benefit from more behavioral context (e.g., performance notes). The description is mostly complete but has minor gaps in transparency.

  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 100%, so the schema already documents all parameters (text, audio_base64, language) with clear descriptions. The description does not add any additional meaning or context beyond what the schema provides, such as explaining parameter interactions or constraints. Baseline 3 is appropriate as the schema handles the heavy lifting.

  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 a specific verb ('Assess') and resource ('pronunciation'), specifying what it evaluates (accuracy of pronunciation against given text). It distinguishes itself from sibling tools like transcribe_speech (which converts speech to text) and synthesize_speech (which generates speech), focusing on assessment rather than transcription or synthesis.

  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 by mentioning what the tool returns (scores and feedback), but does not explicitly state when to use it versus alternatives like transcribe_speech or list_voices. It provides context through the return values but lacks explicit guidance on scenarios or exclusions, such as when audio quality might affect results.

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

• synthesize_speechA
  4/5.0
  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: it returns base64-encoded MP3 audio, which is valuable context not in the schema. However, it doesn't mention potential limitations like rate limits, authentication needs, file size constraints, or error conditions, leaving gaps for a mutation tool.

  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, followed by return format and usage tip. All three sentences earn their place: the first defines the tool, the second specifies output format, and the third provides actionable guidance. Zero waste, appropriately sized.

  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 moderate complexity (text-to-speech conversion), 100% schema coverage, and presence of an output schema (implied by context signals), the description is largely complete. It covers purpose, output format, and voice reference. However, as a mutation tool with no annotations, it could benefit from more behavioral context like error handling or limitations.

  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 100%, so the schema fully documents all three parameters (text, voice, speed). The description adds no additional parameter semantics beyond what's in the schema, such as explaining voice ID formats or speed effects. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 verb ('Convert') and resource ('text to natural-sounding speech audio'), distinguishing it from siblings like assess_pronunciation (evaluation), list_voices (listing), and transcribe_speech (speech-to-text). It precisely communicates the core transformation function.

  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 by mentioning list_voices for voice options, which helps guide usage. However, it doesn't explicitly state when to use this tool versus alternatives like transcribe_speech (reverse operation) or assess_pronunciation (quality assessment), nor does it mention any exclusions or prerequisites for use.

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