Enhanced MCP server for interactive user feedback and command execution in AI-assisted development, featuring dual interface support (Web UI and Desktop Application) with intelligent environment detection and cross-platform compatibility.
Two tools with minimal documentation. interactive_feedback has a bilingual description (Chinese/English) but lacks parameter descriptions, the schema shows three input fields (interactive_feedback, command_logs, images) with only type and description keys, but no guidance on format, constraints, or when to use each. get_system_info has no parameters but its description is vague ('Get system environment information'). Neither tool documents output schema, error handling, or recovery paths. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) present. Naming is acceptable (verb_noun pattern), but descriptions fall short of the 50-200 char LLM-optimized baseline and lack actionable context.
獲取系統環境資訊 (Get system environment information)
收集用戶互動回饋 (Collect interactive user feedback)
Parameter descriptions missing. interactive_feedback schema defines three input fields (interactive_feedback, command_logs, images) with type and description keys, but descriptions are empty or minimal. LLMs cannot infer parameter meaning from names alone, 'interactive_feedback' could mean user text, structured JSON, or a file reference.
Output schema not documented. Neither tool specifies what fields or structure the response contains. LLMs cannot plan downstream tool calls or extract relevant data without knowing the response shape.
No error handling or recovery guidance. Tools do not document what errors can occur, whether they are retryable, or what the LLM should do next. A bare error response leaves the agent unable to self-correct.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | <=2025-11-25 | v2 |
Tool descriptions too short and lack context. get_system_info description is 40 chars; interactive_feedback is 60 chars (bilingual). Baseline for A+ tools is 50-200 chars with explicit WHAT/WHEN/WHY. Current descriptions do not guide LLM selection or explain prerequisites.
No tool annotations. Neither tool declares readOnlyHint, destructiveHint, or idempotentHint. interactive_feedback has WRITE risk but no annotation; get_system_info is READ_ONLY but not marked. Agents cannot reason about side effects or retry safety.