Fast Intercom
About
A high-performance MCP server for analyzing Intercom conversations, offering speeds up to 100x faster than the REST API.
Details
- Author
- evolsb
- Categories
- Communication, Productivity
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Setup
Install Fast Intercom in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/evolsb/FastIntercomMCP
Follow the installation instructions in the repository README, then restart your MCP client.
A high-performance MCP server for analyzing Intercom conversations, offering speeds up to 100x faster than the REST API.
High-performance Model Context Protocol (MCP) server for Intercom conversation analytics. Provides fast, local access to Intercom conversations through intelligent caching and background synchronization.
- 🚀 Fast Local Access: Sub-100ms response times for conversation searches
- 🧠 Intelligent Sync: Request-triggered background updates ensure fresh data
- 💾 Efficient Storage: SQLite-based local storage (~2KB per conversation)
- 🔍 Powerful Search: Natural language timeframes and text search
- ⚡ MCP Integration: Direct integration with Claude Desktop and MCP clients
# Clone and install git clone <repository-url> cd fast-intercom-mcp python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -e .
# Initialize with your Intercom credentials fast-intercom-mcp init # Check status fast-intercom-mcp status # Sync conversation history fast-intercom-mcp sync --force --days 7
Add to your Claude Desktop configuration (~/.config/claude/claude_desktop_config.json):
{ "mcpServers": { "fast-intercom-mcp": { "command": "fast-intercom-mcp", "args": ["start"], "env": { "INTERCOM_ACCESS_TOKEN": "your_token_here" } } } }
fast-intercom-mcp status # Show server status and statistics fast-intercom-mcp sync # Incremental sync of recent conversations fast-intercom-mcp sync --force --days 7 # Force sync last 7 days fast-intercom-mcp start # Start MCP server fast-intercom-mcp logs # View recent log entries fast-intercom-mcp reset # Reset all data
Once connected to Claude Desktop, you can ask questions like:
- "Search for conversations about billing in the last 7 days"
- "Show me customer conversations from yesterday"
- "What's the status of the FastIntercom server?"
- "Get conversation details for ID 123456789"
INTERCOM_ACCESS_TOKEN=your_token_here FASTINTERCOM_LOG_LEVEL=INFO FASTINTERCOM_MAX_SYNC_AGE_MINUTES=5 FASTINTERCOM_BACKGROUND_SYNC_INTERVAL=10
Located at~/.fast-intercom-mcp/config.json:
{ "log_level": "INFO", "max_sync_age_minutes": 5, "background_sync_interval_minutes": 10, "initial_sync_days": 30 }
FastIntercom uses a sophisticated caching strategy:
- Immediate Response: MCP requests return data instantly from local cache
- Background Sync: Stale timeframes trigger background updates
- Smart Triggers: System learns from request patterns to optimize sync timing
- Fresh Data: Next request gets updated data from background sync
- Database: SQLite with optimized schema for fast searches
- Sync Service: Background service with intelligent refresh logic
- MCP Server: Model Context Protocol implementation
- CLI Interface: Command-line tools for management and monitoring
# Unit tests pytest tests/ # Integration test (requires API key) ./scripts/run_integration_test.sh # Docker test ./scripts/test_docker_install.sh
# Full unit test suite with coverage pytest tests/ --cov=fast_intercom_mcp # Integration test with performance report ./scripts/run_integration_test.sh --performance-report # Docker clean install test ./scripts/test_docker_install.sh --with-api-test # Performance benchmarking ./scripts/run_performance_test.sh
- Fast Check: Runs on every PR (unit tests, linting, imports)
- Integration Test: Manual/weekly trigger with real API data
- Docker Test: On releases and deployment validation
- docs/TESTING.md- Complete testing guide
- docs/INTEGRATION_TESTING.md- Integration test procedures
- scripts/README.md- Test script documentation
# Install in development mode pip install -e . # Run with verbose logging fast-intercom-mcp --verbose status # Monitor logs in real-time tail -f ~/.fast-intercom-mcp/logs/fast-intercom-mcp.log
- Response Time: <100ms for cached queries
- Storage Efficiency: ~2KB per conversation average
- Sync Speed: 10-50 conversations/second
- Memory Usage: <100MB for server process
- Small workspace: 100-500 conversations, ~5-25 MB
- Medium workspace: 1,000-5,000 conversations, ~50-250 MB
- Large workspace: 10,000+ conversations, ~500+ MB
- Verify your Intercom access token
- Check token permissions (read conversations required)
- Test:curl -H "Authorization: Bearer YOUR_TOKEN" https://api.intercom.io/me
- Stop any running FastIntercom processes:ps aux | grep fast-intercom-mcp
- Check log file:~/.fast-intercom-mcp/logs/fast-intercom-mcp.log
- Verify Claude Desktop config JSON syntax
- Restart Claude Desktop after configuration changes
- Check that thefast-intercom-mcpcommand is available in PATH
fast-intercom-mcp --verbose start # Enable verbose logging export FASTINTERCOM_LOG_LEVEL=DEBUG # Set debug level
Search conversations with flexible filters.
- query(string): Text to search in conversation messages
- timeframe(string): Natural language timeframe ("last 7 days", "this month", etc.)
- customer_email(string): Filter by specific customer email
- limit(integer): Maximum conversations to return (default: 50)
Get full details of a specific conversation.
- conversation_id(string, required): Intercom conversation ID
- force(boolean): Force full sync even if recent data exists
- Fork the repository
- Create a feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request
MIT License - seeLICENSEfile for details.
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