LogAnalyzer MCP Server

# Cursor AI Integration Guide This guide walks you through integrating the LogAnalyzer MCP Server with Cursor AI for intelligent log analysis directly in your editor. ## Prerequisites - Cursor AI installed - Node.js 18+ - Gemini API key from [Google AI Studio](https://makersuite.google.com/app/apikey) ## Quick Setup ### 1. Install LogAnalyzer MCP Server ```bash # Clone the repository git clone <repository-url> cd loganalyzer-mcp # Install dependencies npm install # Set up environment echo "GEMINI_API_KEY=your_api_key_here" > .env # Build the project npm run build # Test the installation npm run validate ``` ### 2. Configure Cursor AI Open Cursor AI settings and add the MCP server configuration: **Settings Location**: `Cursor > Preferences > Features > Model Context Protocol` Add this configuration: ```json { "mcpServers": { "loganalyzer": { "command": "node", "args": [ "/absolute/path/to/loganalyzer-mcp/dist/src/server.js" ], "env": { "GEMINI_API_KEY": "your_gemini_api_key_here" } } } } ``` **Windows Example:** ```json { "mcpServers": { "loganalyzer": { "command": "node", "args": [ "C:\\Projects\\loganalyzer-mcp\\dist\\src\\server.js" ], "env": { "GEMINI_API_KEY": "your_gemini_api_key_here" } } } } ``` **macOS/Linux Example:** ```json { "mcpServers": { "loganalyzer": { "command": "node", "args": [ "/Users/yourname/projects/loganalyzer-mcp/dist/src/server.js" ], "env": { "GEMINI_API_KEY": "your_gemini_api_key_here" } } } } ``` ### 3. Restart Cursor AI Close and reopen Cursor AI to load the MCP server configuration. ## Usage Examples ### Analyzing Log Files 1. **Open a log file** in Cursor AI 2. **Select the log content** you want to analyze 3. **Ask Cursor**: "Analyze these logs for errors" **Example prompt:** ``` Analyze this log content for errors and provide debugging suggestions: [Selected log content] ``` ### Real-time Log Monitoring **Ask Cursor**: "Start monitoring /var/log/app.log for new errors" Cursor will use the `watch_log_file` tool to monitor the file and alert you to new issues. ### Getting Recent Errors **Ask Cursor**: "Show me the last 5 errors from monitored log files" ### Advanced Analysis **Example prompts for Cursor:** - "What's causing the database connection timeouts in these logs?" - "Find all authentication failures and suggest security improvements" - "Analyze the performance bottlenecks shown in these application logs" - "Compare error patterns between these two log files" ## Available MCP Tools Cursor AI can use these tools through the LogAnalyzer MCP server: ### `analyze_log` - **Purpose**: Analyze log content with AI - **Usage**: Select log text and ask for analysis - **Parameters**: - `logText`: Log content to analyze - `logFormat`: Format hint (auto/json/plain) - `contextLines`: Context lines around errors ### `watch_log_file` - **Purpose**: Monitor log files for new errors - **Usage**: "Monitor [file path] for errors" - **Parameters**: - `filePath`: Path to log file - `pollInterval`: Check interval in milliseconds ### `stop_watching` - **Purpose**: Stop monitoring a file - **Usage**: "Stop watching [file path]" ### `list_watched_files` - **Purpose**: Show all monitored files - **Usage**: "List all watched log files" ### `get_recent_errors` - **Purpose**: Get recent error analysis - **Usage**: "Show recent errors" or "Get last 10 errors" ## Troubleshooting ### MCP Server Not Found **Error**: "MCP server 'loganalyzer' not found" **Solutions**: 1. Check the file path in your configuration 2. Ensure the project is built: `npm run build` 3. Verify Node.js is in your PATH 4. Restart Cursor AI after configuration changes ### API Key Issues **Error**: "GEMINI_API_KEY is required" **Solutions**: 1. Get API key from [Google AI Studio](https://makersuite.google.com/app/apikey) 2. Add it to your MCP configuration 3. Verify the key is valid and has quota remaining ### Permission Errors **Error**: "Cannot watch file: Permission denied" **Solutions**: 1. Check file permissions: `ls -la /path/to/logfile` 2. Run Cursor with appropriate permissions 3. Use absolute paths in file references ### Connection Timeouts **Error**: "Failed to analyze logs: Request timeout" **Solutions**: 1. Check internet connectivity 2. Reduce log size (large logs take longer) 3. Verify Gemini API service status ## Advanced Configuration ### Custom Environment Variables ```json { "mcpServers": { "loganalyzer": { "command": "node", "args": ["/path/to/loganalyzer-mcp/dist/src/server.js"], "env": { "GEMINI_API_KEY": "your_key", "LOG_LEVEL": "debug", "MAX_FILE_SIZE": "20MB", "WATCH_INTERVAL": "500", "MAX_CONTEXT_TOKENS": "10000" } } } } ``` ### Multiple Server Instances You can run multiple instances for different purposes: ```json { "mcpServers": { "loganalyzer-prod": { "command": "node", "args": ["/path/to/loganalyzer-mcp/dist/src/server.js"], "env": { "GEMINI_API_KEY": "prod_key", "LOG_LEVEL": "error" } }, "loganalyzer-dev": { "command": "node", "args": ["/path/to/loganalyzer-mcp/dist/src/server.js"], "env": { "GEMINI_API_KEY": "dev_key", "LOG_LEVEL": "debug" } } } } ``` ## Best Practices ### 1. Log File Selection - Focus on error-specific logs for better analysis - Use recent logs (last 24-48 hours) for relevance - Avoid very large log files (>50MB) for performance ### 2. Effective Prompts - Be specific about what you're looking for - Include context about when issues occurred - Ask follow-up questions based on initial analysis ### 3. Security - Never commit API keys to version control - Use environment variables for sensitive configuration - Monitor API usage to avoid unexpected charges ### 4. Performance - Use file watching for active monitoring - Analyze smaller log sections for faster results - Stop watching files when monitoring is no longer needed ## Example Workflow 1. **Development**: Monitor application logs while coding ``` "Start watching ./logs/app.log for new errors" ``` 2. **Debugging**: Analyze specific error scenarios ``` "Analyze these database connection errors and suggest fixes" ``` 3. **Production**: Monitor critical system logs ``` "Watch /var/log/nginx/error.log and alert me to critical issues" ``` 4. **Analysis**: Compare before/after deployment ``` "Compare error patterns in these two log files from before and after deployment" ``` ## Support - **Issues**: Report bugs in the GitHub repository - **Documentation**: Check the main README.md - **Community**: Join discussions in GitHub Discussions - **Updates**: Watch the repository for new releases ## Next Steps - Try the example prompts above - Experiment with different log formats - Set up monitoring for your critical applications - Explore advanced analysis techniques with AI assistance