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MCP server for validating translation quality, detecting terminology mismatches, truncation risks, and fluency issues in multilingual content
Single tool 'execute_skill_action' has a reasonable name (verb_noun pattern) and a 95-character description that explains the core function. However, the input schema is present but severely underdeveloped: three string parameters with descriptions, but no enums, format constraints, ranges, or validation guidance. The output schema is completely absent from the MCP server definition, only visible in the client mock, not in the actual tool registration. No error handling, no recovery guidance, no security considerations. The server itself is a stub that does not actually invoke the client or validate inputs.
Execute localization quality validation on translated content
Tool definition is inferred from client.py mock, not explicitly registered in mcp_server.py. No actual tool invocation, parameter validation, or error handling visible in the server code.
Output schema completely absent from MCP server. Only visible in client.py mock return statement. LLMs cannot plan downstream actions without knowing response structure (quality_score, issues_found, fluency_rating, ready_for_publish, estimated_fix_time_minutes).
Input parameters lack constraints. 'target_language' accepts any string (should be enum: fr, es, de, etc.). 'source_text' and 'translated_text' have no length limits, format validation, or encoding requirements.
No error handling or recovery guidance. If validation fails (e.g., unsupported language, empty text), the tool provides no actionable error message or next steps for the LLM.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | 2026-07-28+ | v2 |
mcp_server.py is a non-functional stub. It prints a JSON status message but does not implement the MCP protocol, handle tool calls, or invoke the actual validation logic from client.py.