DevMind MCP Server has 5 tools with basic schemas and descriptions, but significant quality gaps. All tools have descriptions (10-60 chars, below the 50-200 char baseline for LLM optimization) and input schemas are present but minimal. No parameter descriptions exist, critical for LLM tool selection. Tools lack error handling guidance, output schemas are undocumented, and there is no evidence of idempotency, pagination, or composition patterns. The server reads code but provides no structured recovery paths when analysis fails.

• Expand all tool descriptions to 80-150 chars. Include WHAT the tool does, WHEN to use it (vs. similar tools), and WHAT it returns. Example: 'Search for functions, classes, and code patterns by name or keyword. Use this to locate specific implementations before calling get_function_context. Returns matching items with file paths and line numbers.'
• Add descriptions to all input parameters. For 'query' in search_codebase: 'Search term (function name, keyword, or pattern). Supports partial matches. E.g., "parse_json" or "error handling".' For 'file_path': 'Relative or absolute path to the file. E.g., "src/utils.py" or "./analyzers/git_analyzer.py".'
• Document output schemas for each tool. Example for search_codebase: 'Returns {"results": [{"name": string, "type": "function"|"class", "file": string, "line": number}], "total": number}'. This enables LLMs to extract and chain results.
• Add error handling with recovery guidance. Example: 'If function_name is not found, return {"error": "Function not found", "suggestion": "Try search_codebase() with a partial name first"}'. Categorize errors as retryable vs. user-fixable.
• Add limit and offset parameters to search_codebase. Default limit=20, max=100. Document: 'Returns up to 20 results per page. Use offset to paginate. Total count included in response.' This prevents context window exhaustion.