ask-expert-consult-mcp-wisdom-cascade

# ask-expert-consult-mcp > Skill by [ara.so](https://ara.so) — MCP Skills collection. An MCP (Model Context Protocol) server that provides AI-powered expert consultation capabilities through OpenRouter's model routing. This server enables Claude Desktop, Cursor, and other MCP-compatible AI agents to request structured expert guidance, code reviews, and architectural advice by leveraging multiple AI models through a unified interface. ## What It Does The ask-expert-consult-mcp server acts as a bridge between your coding environment and OpenRouter's AI model ecosystem. It provides: - **Expert consultation requests** with automatic domain categorization - **Multi-model AI routing** for specialized guidance (frontend, backend, DevOps, etc.) - **Context enrichment** that appends relevant documentation and patterns - **Structured response formats** including code diffs, walkthroughs, and architecture suggestions - **Knowledge persistence** through a local wisdom cache - **Multilingual query support** with automatic language detection ## Installation ### Prerequisites - Node.js 18+ or Python 3.10+ - OpenRouter API key ([get one here](https://openrouter.ai/keys)) - Claude Desktop or Cursor IDE with MCP support ### Setup for Claude Desktop 1. **Clone and install the server:** ```bash git clone https://github.com/pakgik01/ask-expert-consult-mcp.git cd ask-expert-consult-mcp npm install # or pip install -r requirements.txt ``` 2. **Configure environment variables:** Create a `.env` file in the project root: ```bash OPENROUTER_API_KEY=your_openrouter_api_key_here WISDOM_DB_PATH=./wisdom_cache.db DEFAULT_MODEL=anthropic/claude-3.5-sonnet CONFIDENCE_THRESHOLD=0.75 ``` 3. **Add to Claude Desktop config:** Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%/Claude/claude_desktop_config.json` (Windows): ```json { "mcpServers": { "ask-expert-consult": { "command": "node", "args": ["/absolute/path/to/ask-expert-consult-mcp/src/index.js"], "env": { "OPENROUTER_API_KEY": "your_openrouter_api_key_here" } } } } ``` For Python implementation: ```json { "mcpServers": { "ask-expert-consult": { "command": "python", "args": ["/absolute/path/to/ask-expert-consult-mcp/src/server.py"], "env": { "OPENROUTER_API_KEY": "your_openrouter_api_key_here" } } } } ``` 4. **Restart Claude Desktop** to load the MCP server. ### Setup for Cursor IDE Add to your Cursor MCP settings (`.cursor/mcp.json`): ```json { "mcpServers": { "ask-expert-consult": { "command": "node", "args": ["/absolute/path/to/ask-expert-consult-mcp/src/index.js"], "env": { "OPENROUTER_API_KEY": "${OPENROUTER_API_KEY}" } } } } ``` ## Core MCP Tools The server exposes the following MCP tools that AI agents can invoke: ### 1. `ask_expert` Request expert guidance on a coding problem or architectural decision. **Parameters:** - `question` (string, required): The query or problem description - `code_snippet` (string, optional): Relevant code context - `domain` (string, optional): Specific domain (frontend, backend, devops, database, security) - `language` (string, optional): Programming language or framework - `difficulty` (string, optional): junior, intermediate, senior **Example invocation:** ```typescript // AI agent calls this tool internally const response = await callTool("ask_expert", { question: "How do I prevent race conditions in this async function?", code_snippet: ` async function updateUserBalance(userId, amount) { const user = await db.users.findById(userId); user.balance += amount; await user.save(); } `, domain: "backend", language: "javascript", difficulty: "intermediate" }); ``` **Response structure:** ```json { "guidance": "Detailed step-by-step solution...", "code_diff": "Suggested code changes with before/after...", "alternatives": ["Alternative approach 1", "Alternative approach 2"], "confidence_score": 0.92, "related_patterns": ["Transaction pattern", "Optimistic locking"], "wisdom_id": "wsd_a7b3c9d2" } ``` ### 2. `search_wisdom` Search previously answered questions in the local knowledge cache. **Parameters:** - `query` (string, required): Search terms - `domain` (string, optional): Filter by domain - `limit` (number, optional): Maximum results (default: 5) **Example:** ```typescript const cached = await callTool("search_wisdom", { query: "race condition async javascript", domain: "backend", limit: 3 }); ``` ### 3. `enrich_context` Enhance a code question with relevant documentation and patterns before asking. **Parameters:** - `code_snippet` (string, required): Code to analyze - `language` (string, required): Programming language - `include_docs` (boolean, optional): Append official documentation excerpts **Example:** ```typescript const enriched = await callTool("enrich_context", { code_snippet: "const result = await fetch(url).then(r => r.json());", language: "javascript", include_docs: true }); ``` ## Usage Patterns ### Pattern 1: Quick Code Review When a user asks for code feedback: ```typescript // User: "Can you review this React component?" // Agent workflow: // 1. Enrich the context const context = await callTool("enrich_context", { code_snippet: userProvidedCode, language: "javascript", include_docs: false }); // 2. Ask expert with enriched context const review = await callTool("ask_expert", { question: "Please review this React component for best practices and potential issues", code_snippet: context.enriched_code, domain: "frontend", language: "react" }); // 3. Present structured feedback to user ``` ### Pattern 2: Architecture Consultation For high-level design questions: ```typescript // User: "How should I structure a microservices API gateway?" // Agent workflow: // 1. Check wisdom cache first const cached = await callTool("search_wisdom", { query: "microservices API gateway architecture", domain: "backend" }); if (cached.results.length > 0) { // Present cached wisdom with citation } else { // 2. Request fresh expert guidance const guidance = await callTool("ask_expert", { question: "How should I structure a microservices API gateway for a Node.js backend?", domain: "backend", language: "nodejs", difficulty: "senior" }); } ``` ### Pattern 3: Multi-Domain Consultation For complex questions spanning multiple domains: ```typescript // User: "How do I optimize this full-stack feature?" // Agent workflow: // 1. Ask frontend expert const frontendAdvice = await callTool("ask_expert", { question: "Optimize this React component rendering", code_snippet: reactCode, domain: "frontend" }); // 2. Ask backend expert const backendAdvice = await callTool("ask_expert", { question: "Optimize this database query", code_snippet: sqlQuery, domain: "database" }); // 3. Synthesize both responses ``` ## Configuration Options ### Environment Variables ```bash # Required OPENROUTER_API_KEY=sk-or-v1-... # Optional - Model Selection DEFAULT_MODEL=anthropic/claude-3.5-sonnet FRONTEND_MODEL=anthropic/claude-3-opus BACKEND_MODEL=openai/gpt-4-turbo DATABASE_MODEL=anthropic/claude-3.5-sonnet # Optional - Behavior CONFIDENCE_THRESHOLD=0.75 # Minimum confidence for auto-responses WISDOM_DB_PATH=./wisdom_cache.db # Local knowledge cache location MAX_CONTEXT_LENGTH=8000 # Maximum token context per request CACHE_TTL_DAYS=90 # Days to keep wisdom entries AUTO_TRANSLATE=true # Enable multilingual detection # Optional - Privacy ANONYMIZE_AFTER_DAYS=90 # Auto-anonymize old queries RETAIN_RAW_RESPONSES=false # Don't store raw OpenRouter responses ``` ### Model Routing Configuration Create a `wisdom.config.json` for advanced model routing: ```json { "domainModels": { "frontend": "anthropic/claude-3-opus", "backend": "openai/gpt-4-turbo", "devops": "anthropic/claude-3.5-sonnet", "database": "anthropic/claude-3.5-sonnet", "security": "openai/gpt-4-turbo", "general": "anthropic/claude-3.5-sonnet" }, "fallbackModel": "anthropic/claude-3-haiku", "maxRetries": 2, "timeout": 30000 } ``` ## Real-World Examples ### Example 1: Debugging Async Issue ```typescript // User asks: "Why is my async function not waiting?" const response = await callTool("ask_expert", { question: "This async function returns undefined instead of waiting for the promise", code_snippet: ` function getData() { return fetch('/api/data') .then(res => res.json()); } async function processData() { const data = getData(); // Missing await! console.log(data); // Logs Promise, not data } `, domain: "backend", language: "javascript", difficulty: "junior" }); // Response includes: // - Explanation of missing await keyword // - Corrected code with await // - Alternative using .then() pattern // - Related patterns: "Promise chaining", "Async/await best practices" ``` ### Example 2: Security Review ```typescript // User asks: "Is this authentication secure?" const response = await callTool("ask_expert", { question: "Review this authentication implementation for security vulnerabilities", code_snippet: ` app.post('/login', (req, res) => { const { username, password } = req.body; const user = db.query('SELECT * FROM users WHERE username = ? AND password = ?', [username, password]); if (user) { req.session.userId = user.id; res.json({ success: true }); } }); `, domain: "security", language: "javascript", difficulty: "intermediate" }); // Response flags: // - Missing password hashing // - SQL injection vulnerability (though parameterized) // - No rate limiting // - Suggests bcrypt, prepared statements, express-rate-limit ``` ### Example 3: Performance Optimization ```typescript // User asks: "How do I optimize this database query?" const response = await callTool("ask_expert", { question: "This query is slow on large datasets. How can I optimize it?", code_snippet: ` SELECT u.*, p.*, c.* FROM users u LEFT JOIN posts p ON u.id = p.user_id LEFT JOIN comments c ON p.id = c.post_id WHERE u.created_at > '2024-01-01' ORDER BY u.created_at DESC; `, domain: "database", language: "sql", difficulty: "intermediate" }); // Response suggests: // - Add index on users.created_at // - Paginate results with LIMIT/OFFSET // - Consider separate queries for N+1 prevention // - Use EXPLAIN ANALYZE to profile ``` ## Troubleshooting ### Server Not Appearing in Claude Desktop 1. **Check config path**: Ensure absolute paths in `claude_desktop_config.json` 2. **Verify API key**: Test key at https://openrouter.ai/ 3. **Check logs**: ```bash tail -f ~/Library/Logs/Claude/mcp*.log # macOS ``` 4. **Restart Claude**: Completely quit and reopen ### "Model not found" Errors - Verify model name in OpenRouter docs: https://openrouter.ai/docs#models - Check your OpenRouter account has credits - Try fallback model: `anthropic/claude-3-haiku` ### Low Confidence Scores If responses frequently have low confidence (<0.75): 1. Provide more code context in `code_snippet` 2. Specify the `domain` explicitly 3. Enrich context first with `enrich_context` tool 4. Lower `CONFIDENCE_THRESHOLD` in config (not recommended) ### Slow Response Times