Project Brain MCP has 4 tools with clear verb-noun naming (scan_repo_structure, identify_key_files, explain_auth_flow, ask_codebase). All tools have descriptions (194 - 250 chars, within baseline 34 - 392 range) and input schemas using Zod. However, critical gaps exist: (1) Parameter descriptions are minimal or missing context. The 'repo_path' param appears in all tools but lacks guidance on format validation or error handling for invalid paths. (2) No output schemas are documented, tools return text responses but LLMs cannot predict structure. (3) Error handling is generic ('Error: {msg}') with no recovery guidance. (4) No parameter constraints (enums, ranges, patterns) despite accepting free-form strings. (5) Tool descriptions lack WHEN/WHY context, they state WHAT but not when to prefer one tool over another. The codebase shows competent implementation (GitHub API integration, relevance scoring, AI-grounded answers) but tool definitions lack the rigor needed for reliable agent composition.

• Document output schemas for all tools. Example: scan_repo_structure returns {tech_stack: string[], architecture: string, files_analyzed: number}. This enables LLMs to plan downstream calls.
• Add validation rules to repo_path parameter: 'Local folder path (must exist and be readable) OR GitHub URL (https://github.com/owner/repo format). Invalid paths return 'Path not found. Verify the folder exists or GitHub URL is correct.'
• Enhance error responses with recovery guidance. Instead of 'Error: ENOENT', return 'Folder not found. Try verify_path() first or provide a GitHub URL instead.'
• Add WHEN/WHY context to tool descriptions. E.g., 'Use scan_repo_structure for a high-level overview; use ask_codebase for specific questions about implementation details.'
• Add max_files parameter to scan_repo_structure, identify_key_files, and explain_auth_flow (default 20 - 50). Document limits in descriptions to prevent context window exhaustion.