Community Research MCP

# START HERE - Claude Desktop Setup ## Quick Setup (2 Minutes) ### Step 1: Run the Installer 1. Double-click `initialize.bat` 2. Wait for dependencies to install 3. Add your API key when the .env file opens 4. Save and close ### Step 2: Restart Claude Desktop - Close Claude Desktop completely - Open it again ### Step 3: Test It! In Claude Desktop, type: ``` use get_server_context ``` You should see available tools and workspace info! Then try: ``` search for Python FastAPI background task solutions ``` --- ## Get FREE API Key You need a Gemini API key (FREE - 1,500 requests/day): 1. Go to: https://makersuite.google.com/app/apikey 2. Click "Create API Key" 3. Copy the key 4. Paste it in `.env` file after `GEMINI_API_KEY=` No credit card required! --- ## Not Working? ### If initialize.bat didn't work: See **CLAUDE_DESKTOP_SETUP.md** for manual setup instructions. You need to: 1. Install dependencies: `pip install mcp fastmcp httpx pydantic` 2. Create `.env` with your API key 3. Edit `%APPDATA%\Claude\claude_desktop_config.json` 4. Add server configuration 5. Restart Claude Desktop Full instructions in **CLAUDE_DESKTOP_SETUP.md** --- ## Common Issues **"Server not found"** - Check: `%APPDATA%\Claude\claude_desktop_config.json` exists - Make sure path in config is FULL path (e.g., `C:\\Users\\...`) - Use double backslashes `\\` in Windows paths **"No module named 'mcp'"** - Run: `pip install mcp fastmcp httpx pydantic` **"No API key configured"** - Check `.env` file has your key: `GEMINI_API_KEY=your_key` - Make sure there's no space after the `=` --- ## Files in This Package - **START_HERE.md** - This file - **initialize.bat** - Auto-installer - **CLAUDE_DESKTOP_SETUP.md** - Detailed setup & troubleshooting - **community_research_mcp.py** - The MCP server - **.env.example** - API key template - **requirements.txt** - Python dependencies - **README.md** - Full documentation --- ## What This Does Searches Stack Overflow, Reddit, GitHub, and Hacker News for real solutions, then uses AI to synthesize findings into actionable recommendations with: - Working code examples - Measurable benefits - Community validation (GitHub stars, SO votes) - Difficulty ratings - Gotchas and warnings **No more AI guessing game!**