Pinot MCP Server

Table of Contents

• Overview
• Features
• Quick Start
• Configuration Reference
• Docker Build
• Claude Desktop Integration
• Try a Prompt
• Security and Vulnerability Reporting
• Developer Notes

Overview

This project is a Python-based Model Context Protocol (MCP) server for interacting with Apache Pinot. It is built using the FastMCP framework. 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.

Features

• Every tool advertises typed input and output JSON Schemas, MCP risk annotations, and failure-recovery guidance for agent planning.
• Large query, table, segment-name, and segment-metadata responses use bounded pages with continuation metadata instead of returning unbounded agent context.
• Read-only SQL is parsed and enforced before execution; validation, permission, and transient connectivity errors are surfaced as actionable MCP errors.
• Every mutating tool supports dry_run; always preview the exact target and payload before applying. Applying requires the preview's short-lived, one-time confirmation_token, including for table-filter reloads. A preview is not a guarantee that Pinot will accept the later write.
• Single-purpose schema and table-config inspection tools avoid ambiguous combined operations: use get_schema and get_table_config independently.

MCP Tool Contract

Tool names are case-sensitive and use underscores. Version 4 renamed four tools to make every operation verb-first; clients using the former noun-first names must update their calls.

For every schema, table-config, or table-filter change, first call the same tool with dry_run=true, present the preview to the user, and call it with dry_run=false and the preview's one-time confirmation_token only after confirmation. Editing a table-filter file after preview invalidates its token. Pinot performs authoritative validation during table/schema apply calls, so a write can still fail after a successful preview.

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

Configuration Reference

The server loads configuration from environment variables and from a .env file found from the current working directory. Process environment variables take precedence over .env, so deployment-time settings cannot be silently replaced by a checked-out file.

Common Profiles

Pinot Connection

MCP Server