Wisdom Layer MCP

# Wisdom Layer MCP A metacognitive advisor for Claude powered by LearnLM. ## Overview The Wisdom Layer MCP is a specialized Model Context Protocol server designed to enhance Claude's reasoning capabilities. It creates a "wisdom layer" that acts as a strategic questioner, advisor, and complexity reducer for Claude, helping it to avoid recurring mistakes and achieve better results. ## Features - **Strategic Metacognitive Advisor**: Analyzes Claude's thinking process and provides targeted advice - **Plan Distillation**: Forces ultra-simplified summaries for clarity and focus - **Mistake Tracking**: Learns from past mistakes to avoid future ones - **LearnLM Integration**: Powered by Google's LearnLM/Gemini API for metacognitive guidance ## Tools ### 1. wisdom_advise A constraint-free strategic advisor that works like a challenging mentor: - Takes raw, unfiltered context to avoid Claude's cognitive biases - Provides user alignment checks and complexity reduction - Offers pattern-breaking questions and perspectives - Suggests appropriate MCP tools to use - Learns from past mistake patterns ``` <use_mcp_tool> <server_name>wisdom</server_name> <tool_name>wisdom_advise</tool_name> <arguments> { "plan": "My current plan is...", "userRequest": "Original request from user", "thinkingLog": "Raw sequential thinking output", "availableTools": ["tool1", "tool2"] } </arguments> </use_mcp_tool> ``` ### 2. wisdom_canvas A distillation tool for final plans: - Forces ultra-simplified plan representation - Includes "why" section to justify approach - Creates a checkpoint before implementation - Serves as a reference during execution ``` <use_mcp_tool> <server_name>wisdom</server_name> <tool_name>wisdom_canvas</tool_name> <arguments> { "plan": "Detailed plan to distill", "userRequest": "Original request from user" } </arguments> </use_mcp_tool> ``` ### 3. wisdom_log A mistake tracking system: - One-sentence descriptions of mistakes made and corrected - Categorizes and tallies recurring mistakes - Creates a persistent learning feedback loop - Read by LearnLM to provide targeted guidance ``` <use_mcp_tool> <server_name>wisdom</server_name> <tool_name>wisdom_log</tool_name> <arguments> { "mistake": "One-sentence description of the mistake", "category": "mistake-category", "solution": "How it was corrected" } </arguments> </use_mcp_tool> ``` ## Installation ### Prerequisites - Node.js (v18.0.0 or higher) - npm (v7.0.0 or higher) - A Gemini API key for LearnLM functionality ### Installation Steps ```bash # Install globally npm install -g wisdom-layer-mcp # Or run directly npx wisdom-layer-mcp ``` ### MCP Configuration Add the server to your MCP settings file. For Claude/Cline, this is typically located at: - For Claude Desktop: `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) - For VSCode Cline: `~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json` (Linux) Add the following configuration: ```json { "mcpServers": { "wisdom": { "command": "wisdom-layer-mcp", "args": [], "env": { "GEMINI_API_KEY": "your-gemini-api-key" }, "disabled": false, "autoApprove": [] } } } ``` ## Usage Pattern The ideal usage pattern for Claude: 1. Claude uses sequential thinking to formulate an initial plan 2. Claude calls wisdom_advise with raw context 3. Claude refines its approach based on LearnLM's advice 4. Claude finalizes with wisdom_canvas for ultra-clarity 5. Claude implements the solution 6. Claude logs any lessons learned in wisdom_log This creates a metacognitive layer that helps Claude think better about its own thinking. ## Development ```bash # Clone the repository git clone https://github.com/yourusername/wisdom-layer-mcp.git cd wisdom-layer-mcp # Install dependencies npm install # Build npm run build # Start development mode npm run dev ``` ## License MIT