quality_score

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

No annotations are provided, so the description carries the full transparency burden. It details what metrics are analyzed and what issues are detected, but it does not disclose the output format (e.g., scores, thresholds, reports) or any behavioral constraints (e.g., supported audio duration, processing side effects). This lacks key information for an agent to anticipate the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose followed by concrete details. Every word adds value, with no redundancy or filler. It is highly efficient and well-structured.

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

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

The tool has moderate complexity, no output schema, and no annotations. The description explains what it does and what it detects, but it omits the return format and any usage limitations. Compared to sibling tools, it lacks differentiation cues and could benefit from explicitly stating output criteria. Overall sufficient but with clear gaps.

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

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

The schema covers 100% of the single parameter (audio_path) with a description of the path and accepted file types. The tool description adds context that the audio is analyzed for speech quality, but no additional semantics beyond the schema. Baseline of 3 applies since schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes speech quality metrics of an audio file, listing concrete metrics (pitch variation, energy, pacing, silence ratio) and outcomes (detects robotic tone, monotone speech, audio issues). It uses a specific verb and resource, distinguishing it from sibling tools like transcribe (speech-to-text) and compare_tts (comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context: if you need to evaluate speech quality, this tool is appropriate. However, it does not explicitly state when to use it versus alternatives like analyze_tts or compare_tts, nor does it mention any exclusions or prerequisites. Guidance is implied but not explicit.

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