A high-performance MCP server for analyzing Intercom conversations with fast, local access via caching and background sync.
High-performance Model Context Protocol (MCP) server for Intercom conversation analytics. Provides fast, local access to Intercom conversations through intelligent caching and background synchronization.
# 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:
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:
# 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
For detailed testing procedures, see:
docs/TESTING.md
- Complete testing guidedocs/INTEGRATION_TESTING.md
- Integration test proceduresscripts/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
Connection Failed
curl -H "Authorization: Bearer YOUR_TOKEN" https://api.intercom.io/me
Database Locked
ps aux | grep fast-intercom-mcp
~/.fast-intercom-mcp/logs/fast-intercom-mcp.log
MCP Server Not Responding
fast-intercom-mcp
command is available in PATHfast-intercom-mcp --verbose start # Enable verbose logging
export FASTINTERCOM_LOG_LEVEL=DEBUG # Set debug level
search_conversations
Search conversations with flexible filters.
Parameters:
query
(string): Text to search in conversation messagestimeframe
(string): Natural language timeframe ("last 7 days", "this month", etc.)customer_email
(string): Filter by specific customer emaillimit
(integer): Maximum conversations to return (default: 50)get_conversation
Get full details of a specific conversation.
Parameters:
conversation_id
(string, required): Intercom conversation IDget_server_status
Get server status and statistics.
Parameters: None
sync_conversations
Trigger manual conversation sync.
Parameters:
force
(boolean): Force full sync even if recent data existsgit checkout -b feature/amazing-feature
)git commit -m 'Add amazing feature'
)git push origin feature/amazing-feature
)MIT License - see LICENSE file for details.
~/.fast-intercom-mcp/logs/fast-intercom-mcp.log
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