MCP Server Whisper

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions the API provider (OpenAI) and key features (model/voice selection), but lacks critical information: whether this is a read-only or mutating operation, what permissions or authentication are needed, rate limits, what happens to the output file, or error handling. For a tool with 6 parameters and no annotations, this is insufficient.

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 a single, well-structured sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded with the main purpose and includes essential details about the API and key parameters. Every element earns its place.

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?

Given the tool's complexity (6 parameters, no annotations, but with an output schema), the description is moderately complete. The output schema existence means return values don't need explanation, but the description lacks crucial context about behavioral aspects, usage scenarios, and parameter details. It's adequate for basic understanding but has significant 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?

Schema description coverage is 0%, so the description must compensate. It mentions 'model and voice selection' which maps to two of the six parameters, but doesn't explain the other four parameters (text_prompt, instructions, speed, output_file_name) or their relationships. The description adds some value but doesn't fully compensate for the schema's lack of descriptions.

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's purpose: 'Create text-to-speech audio using OpenAI's TTS API with model and voice selection.' It specifies the action ('Create text-to-speech audio'), the technology ('OpenAI's TTS API'), and key capabilities ('model and voice selection'). However, it doesn't explicitly differentiate from sibling tools like 'transcribe_audio' or 'convert_audio', which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives. With sibling tools like 'transcribe_audio' (speech-to-text) and 'convert_audio' (format conversion), there's no indication of when this text-to-speech tool is appropriate or what distinguishes it from other audio-related tools on the server.

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