Server Quality Checklist

Tool Scores

• speakC
  2.9/5.0
  Behavior2/5

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

  With no annotations, the description must expose behavioral traits. It only states the conversion action, omitting crucial details such as whether the tool returns a file path, the audio format, model download behavior, or the effect of the optional output_path parameter. This leaves significant unknown side effects.

  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 a single, simple sentence with no extraneous words. It is front-loaded with the core action. However, it is arguably too terse to serve as a complete tool guide, though for conciseness alone it earns a high score.

  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 six parameters, no output schema, and no annotations, the description is insufficiently complete. It lacks mention of the return type, file saving behavior, or operational side effects such as model downloads. This makes it difficult for an agent to know what to expect after invocation.

  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 input schema provides descriptions for all six parameters, achieving 100% coverage. The description adds no parameter-specific information, so it neither enhances nor detracts from the schema's semantics. Baseline 3 is appropriate.

  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 function with a specific verb ('Convert') and resource ('text to speech') plus the engine ('Coqui TTS'), making it easily understandable. However, it does not explicitly differentiate from sibling tools like synthesize_long_text or clone_voice, though the core purpose is distinct enough.

  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?

  No usage guidance is provided. The description does not mention when to prefer this tool over list_models, synthesize_long_text, or clone_voice, nor any exclusions. The agent must infer context from sibling names alone.

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

• clone_voiceB
  3.4/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It only mentions that the XTTS model is required, but nothing about output behavior, side effects, audio format requirements, or whether the voice is stored. This is minimal disclosure for a voice-cloning 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 a single, direct sentence with no unnecessary words. It efficiently communicates the core action and a key requirement.

  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?

  Despite having 4 parameters and no output schema, the description provides only a minimal overview. It lacks essential context about return values, model behavior, prerequisites beyond the model, and how it relates to sibling tools. This is incomplete for a tool of this complexity.

  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% with clear per-parameter descriptions, so the baseline is 3. The tool description adds no additional parameter semantics beyond the phrase 'audio sample', which is already implicit in 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 description clearly states the action ('Clone a voice') and the source ('audio sample'), making the tool's purpose unambiguous. It also distinguishes itself from sibling tools like 'speak' and 'synthesize_long_text' by focusing on voice cloning rather than plain 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 when voice cloning is required, but does not explicitly state when to use this tool instead of alternatives like 'speak' or 'synthesize_long_text'. No exclusions or when-not-to-use guidance is provided.

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

• synthesize_long_textB
  3.4/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It only mentions 'automatic chunking' as a behavior, but does not specify that it converts text to speech, saves an audio file, or what the output is. This is insufficient for a tool with no annotations.

  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 a single sentence that clearly conveys the core purpose and the key feature of automatic chunking. It is concise, front-loaded, and every word contributes to the meaning.

  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?

  Despite the schema covering parameters, the description lacks essential context such as what the tool produces (an audio file), how output_path is used, and any length constraints. Without annotations, this is incomplete for a tool that is a variant of 'speak'.

  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 input schema has 100% coverage, with each parameter described (e.g., 'text' as 'The long text to convert to speech'). The description adds only a hint about chunking behavior, which does not significantly go beyond the schema, so the baseline 3 is appropriate.

  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 'Synthesize longer text with automatic chunking' specifies a clear action (synthesize), resource (longer text), and scope (automatic chunking). The 'longer text' differentiates it from sibling tools like 'speak', which likely handles shorter inputs.

  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 use for longer text, especially with the mention of 'automatic chunking', but it does not explicitly state when to prefer this over 'speak' or other alternatives. There are no exclusion criteria or explicit alternatives mentioned.

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

• list_modelsA
  4/5.0
  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 does not explicitly state that the operation is read-only or side-effect-free, though 'List' implies it. No details on rate limits, auth, or pagination are given, but this is a minimal risk operation.

  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 a single, short sentence with no filler. Every word is meaningful, and the format is front-loaded with the verb and resource.

  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 has no parameters, no output schema, and no annotations, the description is minimal but adequate for a simple listing operation. It does not specify the return format or fields, but the core purpose is clear enough for a low-complexity tool.

  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 zero parameters and schema coverage is 100% (trivially). The description needs no additional parameter detail, so the baseline of 4 applies.

  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 'List available TTS models' uses a specific verb ('List') and resource ('TTS models'), clearly distinguishing this tool from siblings as a read-only discovery operation. It is unambiguous and self-contained.

  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 gives no explicit guidance on when to use this tool versus alternatives, but the name and sibling context imply it should be used before speak/synthesize_long_text to discover model identifiers. Usage is inferred rather than stated.

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