Apache Pinot + mcp-agent: Complete MCP Integration
Apache Pinot is a MCP server that MCP server for Apache Pinot real-time analytics database with SQL query capabilities and metadata access.
When integrated with mcp-agent, you can:
- List and inspect Apache Pinot tables
- View and analyze table segments
- Retrieve and analyze table schemas
This guide provides step-by-step instructions to set up Apache Pinot in mcp-agent, including configuration, examples, and troubleshooting.
What You'll Achieve
After completing this setup:
- Apache Pinot will be fully integrated and operational
- You can use Apache Pinot tools directly in mcp-agent
- All Apache Pinot capabilities will be available for your workflows
- Access to 5 different tools
Prerequisites
Before starting, ensure you have:
- Apache Pinot broker hostname
- Apache Pinot broker port
- mcp-agent installed and configured
- Compatible operating system (Python 3.9+, pip Installation, uv Installation, Temporal (Optional))
Installation
Step 1: Install Apache Pinot
Configuration
Step 2: Configure mcp-agent
- Open mcp-agent settings
- Navigate to MCP server configuration
- Add Apache Pinot server with appropriate settings
- Save and restart if needed
Examples
Once configured, you can use Apache Pinot in mcp-agent:
List All Tables
Get overview of available Pinot tables
Ask mcp-agent: "Show me all the tables available in the Pinot cluster"
Expected Result: List of table names with basic metadata and schema information
Execute Analytics Query
Run SQL query for real-time analytics
Ask mcp-agent: "SELECT COUNT(*) FROM events WHERE timestamp > now() - 3600000"
Expected Result: Query results showing event count for last hour
Analyze Table Structure
Get detailed schema information for a table
Ask mcp-agent: "Show me the schema and structure of the user_events table"
Expected Result: Table schema with column names, types, and index information
Generate Data Visualization
Create histogram plots from query results
Ask mcp-agent: "Create a histogram showing user activity distribution by hour"
Expected Result: Histogram visualization with data aggregated by time buckets
Testing Your Setup
- Launch mcp-agent
- Verify Apache Pinot is available in the tools list
- Test basic Apache Pinot functionality
Troubleshooting
Common Issues
uv Command Not Found
Symptoms: Command not found error, uv: command not found
Cause: uv package manager not installed
Solution:
- Install uv using: curl -LsSf https://astral.sh/uv/install.sh | sh
- Restart terminal or source shell configuration
- Verify installation with: uv --version
Pinot Connection Failed
Symptoms: Connection refused, Broker unreachable, Network timeout
Cause: Invalid Pinot broker configuration or cluster not running
Solution:
- Verify PINOT_BROKER_HOST and PINOT_BROKER_PORT in .env file
- Check Pinot cluster is running and accessible
- Test connection: telnet <broker_host> <broker_port>
- Ensure firewall allows connections to Pinot broker
Environment Variables Not Loaded
Symptoms: Config missing errors, Default values being used
Cause: .env file not found or incorrectly formatted
Solution:
- Create .env file in repository root directory
- Copy from .env.example and modify values
- Ensure no spaces around = in variable assignments
- Restart MCP server after configuration changes
Claude Desktop Not Recognizing Server
Symptoms: Server not appearing, Connection errors in Claude
Cause: Incorrect claude_desktop_config.json configuration
Solution:
- Verify path to uv binary in command field
- Ensure correct path to mcp-pinot repository
- Restart Claude Desktop after configuration changes
- Check Claude Desktop logs for error messages
Import Error - Module Not Found
Symptoms: ModuleNotFoundError: No module named mcp_agent, Import fails
Cause: mcp-agent not installed or wrong Python environment
Solution:
- Verify installation: pip show mcp-agent
- Ensure using correct Python environment/virtualenv
- Reinstall: pip install --upgrade mcp-agent
- Check Python version is 3.9 or higher
MCP Server Connection Failures
Symptoms: Server not starting, Connection timeout, Tool execution errors
Cause: Invalid MCP server configuration or missing dependencies
Solution:
- Verify npx is installed and accessible (required for stdio servers)
- Check MCP server package name and arguments are correct
- Test server independently: npx -y @modelcontextprotocol/server-filesystem /path
- Review agent logs for detailed error messages
- Ensure required environment variables are set
LLM API Authentication Errors
Symptoms: 401 Unauthorized, API key invalid, Provider authentication failed
Cause: Missing or incorrect API keys for LLM provider
Solution:
- Set environment variables: ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.
- Verify API key is valid and has correct permissions
- Check API key format matches provider requirements
- Pass api_key parameter explicitly in Agent initialization
Workflow Execution Failures
Symptoms: Workflow hangs, Timeout errors, Incomplete results
Cause: Workflow pattern misconfiguration or task complexity
Solution:
- Increase timeout values for long-running tasks
- Break complex workflows into smaller steps
- Verify all workflow dependencies are available
- Check Temporal backend is running (for production deployments)
- Review workflow logs for specific error messages
Temporal Backend Connection Issues
Symptoms: Cannot connect to Temporal, Workflow registration fails, Worker errors
Cause: Temporal server not running or misconfigured
Solution:
- Verify Temporal server is running: temporal server health
- Check Temporal connection settings (host, port, namespace)
- Ensure Temporal workers are registered correctly
- Review Temporal server logs for connection errors
- Start local Temporal dev server: temporal server start-dev
Performance Issues with Large Workflows
Symptoms: Slow execution, High memory usage, Timeout errors
Cause: Inefficient workflow design or resource constraints
Solution:
- Use map-reduce pattern for parallelizable tasks
- Implement batching for large data processing
- Enable Temporal backend for better resource management
- Monitor agent memory usage and optimize task size
- Consider streaming responses for large outputs
Apache Pinot not appearing in mcp-agent
Symptoms: Server not listed, Tools not available
Cause: Configuration or installation issue
Solution:
- Verify configuration syntax
- Check Apache Pinot installation
- Restart mcp-agent
- Check logs for error messages
Next Steps
Now that Apache Pinot is integrated with mcp-agent:
- Explore all Apache Pinot capabilities through mcp-agent
- Check out other MCP servers that work with mcp-agent
- Join the MCP community for tips and support
- Consider contributing to Apache Pinot development
Need Help?
- Search for Apache Pinot documentation
- Check the mcp-agent MCP guide
- Join the MCP community discussions