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MCP server for speaker diarization using neural voiceprint technology to identify and separate distinct speakers in multi-speaker audio
Critical failures across all dimensions. The single tool 'execute_skill_action' has a reasonable name and basic parameter schema, but the MCP server implementation is non-functional. The mcp_server.py file does not implement the MCP protocol at all, it merely prints a JSON status message and exits. There is no request/response loop, no tool invocation handling, no error handling, and no actual integration with the client. The tool description is minimal (47 chars, below the 50-200 char baseline for LLM-optimized descriptions). Parameter descriptions are present but generic. Most critically, the server cannot receive or process tool calls from an MCP client.
Execute speaker diarization skill action on multi-speaker audio
MCP server does not implement the protocol. mcp_server.py prints a status message and exits instead of entering a request/response loop. No tool invocation handling exists.
Tool description is too short (47 chars). Baseline for LLM-optimized descriptions is 50-200 chars. Current description lacks context on when to use the tool and what it returns.
No output schema documented. LLMs cannot plan downstream calls or extract required fields from the diarization response (job_id, speaker_count, error_rate, etc.).
No error handling or recovery guidance. If the audio URL is invalid, unreachable, or the diarization fails, the tool provides no actionable error message or next steps for the LLM.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 39 | 2026-07-28+ | v2 |
Parameter descriptions lack format/constraint details. 'max_speakers' has no range (e.g., 1-10), and 'multi_speaker_audio_url' does not specify supported formats or size limits.