Pinot MCP Server

Table of Contents

• Overview

• Features

• Quick Start

• Docker Build

• Claude Desktop Integration

• Try a Prompt

• Developer Notes

Related MCP server: Pinot MCP Server

Overview

This project is a Python-based Model Context Protocol (MCP) server for interacting with Apache Pinot. It is designed to integrate with Claude Desktop to enable real-time analytics and metadata queries on a Pinot cluster.

It allows you to

• List tables, segments, and schema info from Pinot

• Execute read-only SQL queries

• View index/column-level metadata

• Designed to assist business users via Claude integration

• and much more.

Pinot MCP in Action

See Pinot MCP in action below:

Fetching Metadata

Fetching Data, followed by analysis

Prompt: Can you do a histogram plot on the GitHub events against time

Sample Prompts

Once Claude is running, click the hammer 🛠️ icon and try these prompts:

• Can you help me analyse my data in Pinot? Use the Pinot tool and look at the list of tables to begin with.

• Can you do a histogram plot on the GitHub events against time

Quick Start

Prerequisites

Install uv (if not already installed)

uv is a fast Python package installer and resolver, written in Rust. It's designed to be a drop-in replacement for pip with significantly better performance.

curl -LsSf https://astral.sh/uv/install.sh | sh

# Reload your bashrc/zshrc to take effect. Alternatively, restart your terminal
# source ~/.bashrc

Installation

# Clone the repository
git clone https://github.com/startreedata/mcp-pinot.git
cd mcp-pinot
uv pip install -e . # Install dependencies

# For development dependencies (including testing tools), use:
# uv pip install -e .[dev] 

Configure Pinot Cluster

The MCP server expects a uvicorn config style .env file in the root directory to configure the Pinot cluster connection. This repo includes a sample .env.example file that assumes a pinot quickstart setup.

mv .env.example .env

Run the server

uv --directory . run mcp_pinot/server.py

You should see logs indicating that the server is running and listening on STDIO.

Launch Pinot Quickstart (Optional)

Start Pinot QuickStart using docker:

docker run --name pinot-quickstart -p 2123:2123 -p 9000:9000 -p 8000:8000 -d apachepinot/pinot:latest QuickStart -type batch

Query MCP Server

uv --directory . run tests/test_service/test_pinot_quickstart.py

This quickstart just checks all the tools and queries the airlineStats table.

Claude Desktop Integration

Open Claude's config file

vi ~/Library/Application\ Support/Claude/claude_desktop_config.json

Add an MCP server entry

{
  "mcpServers": {
      "pinot_mcp_claude": {
          "command": "/path/to/uv",
          "args": [
              "--directory",
              "/path/to/mcp-pinot-repo",
              "run",
              "mcp_pinot/server.py"
          ],
          "env": {
            // You can also include your .env config here
          }
      }
  }
}

Replace /path/to/uv with the absolute path to the uv command, you can run which uv to figure it out.

Replace /path/to/mcp-pinot with the absolute path to the folder where you cloned this repo.

You could also configure environment variables here instead of the .env file, in case you want to connect to multiple pinot clusters as MCP servers.

Restart Claude Desktop

Claude will now auto-launch the MCP server on startup and recognize the new Pinot-based tools.

Using DXT Extension

Apache Pinot MCP server now supports DXT desktop extensions file

To use it, you first need to install dxt via

npm install -g @anthropic-ai/dxt

then you can run the following commands:

uv pip install -r pyproject.toml --target mcp_pinot/lib
uv pip install . --target mcp_pinot/lib 
dxt pack

After this you'll get a .dxt file in your dir. Double click on that file to install it in claude desktop

Developer

• All tools are defined in the Pinot class in utils/pinot_client.py

Build

Build the project with

pip install -e ".[dev]"

Test

Test the repo with:

pytest

Build the Docker image

docker build -t mcp-pinot .

Run the container

docker run -v $(pwd)/.env:/app/.env mcp-pinot

Note: Make sure to have your .env file configured with the appropriate Pinot cluster settings before running the container.