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An MCP server that dispatches long-running tool actions asynchronously with audio feedback cues to reduce perceived latency during tool execution
This server has a single tool 'execute_skill_action' with critical definition gaps. The tool description is present but vague (58 chars, below the 194-char baseline). No input schema is visible in the source code, the mcp_server.py only prints a JSON status message with no actual tool registration, parameter definitions, or output schema documentation. The client.py and example_usage.py show example usage but do not constitute a formal MCP tool definition. Tool naming is acceptable (verb_noun form), but without visible schema, parameter descriptions, or structured output documentation, the tool cannot be reliably used by an LLM agent. This appears to be a proof-of-concept or stub implementation rather than a production-ready MCP server.
Execute a skill action with async agentic tool dispatching and audio feedback
No input schema visible. Tool registration in mcp_server.py is a stub that prints a JSON status message but does not define tool schemas, parameter types, or descriptions. Cannot verify parameter constraints, types, or required fields.
Tool description is vague and under 100 characters (58 chars). 'Execute a skill action with async agentic tool dispatching and audio feedback' does not explain WHEN to use this tool, WHAT it returns, or HOW it differs from other tools. Baseline is 194 chars.
No parameter descriptions visible. The tool likely accepts parameters (tool_name, estimated_duration_s, etc. from client.py) but none are documented in the MCP server definition. LLMs cannot infer parameter meaning from names alone.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 26 | 2026-07-28+ | v2 |
Output schema not documented. The client.py shows example output (tool_name, execution_mode, audio_progress_cues, user_perceived_latency_reduction_pct, voice_stream_uninterrupted) but this is not formally declared in the MCP server. LLMs need documented return types to plan downstream calls.
No error handling or recovery guidance. Tool description does not explain failure modes, retryability, or what the LLM should do if the tool fails. No error classification (retryable vs. fatal).