API-Market MCP Server

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 mentions the tool uses AI models and requires a follow-up call to 'get audio analysis URL,' but doesn't describe rate limits, authentication needs, error handling, output format, or whether it's read-only or destructive. For an 11-parameter tool with no annotations, this leaves significant behavioral gaps.

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 concise with two sentences: one stating the purpose and one providing a workflow instruction. It's front-loaded with the core function, and both sentences add value (purpose and usage step). There's no unnecessary verbosity, making it efficient in structure.

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 complexity (11 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values (e.g., what the 'request ID' is or the transcription output), parameter semantics, or behavioral traits like error conditions. For a tool with rich input schema but no other structured data, this description lacks sufficient context for effective use.

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 description provides no information about any of the 11 parameters (schema description coverage is 0%). It doesn't explain what parameters like 'compression_ratio_threshold' or 'logprob_threshold' mean, their effects, or typical values. With low schema coverage, the description fails to compensate, leaving parameters largely undocumented.

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 states the tool performs 'sentence wise transcription and analysis of audio, using AI models,' which provides a clear purpose (transcription + analysis). However, it doesn't differentiate from sibling tools like 'Get_audio_analysis_URL' or explain how this transcription differs from other potential audio processing tools in the list. The purpose is clear but lacks sibling differentiation.

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 includes a usage instruction: 'Make sure to call get audio analysis URL with the request ID received from this API,' which implies a workflow dependency. However, it doesn't specify when to use this tool versus alternatives (e.g., other audio or transcription tools), provide exclusions, or explain prerequisites beyond the workflow step. This offers minimal guidance without broader context.

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