StarTree MCP Server for Apache Pinot

StarTree MCP Server for Apache Pinot

StarTree MCP Server for Apache Pinot

Category
访问服务器

README

Pinot MCP Server

Table of Contents

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

Pinot MCP fetching metadata

Fetching Data, followed by analysis

Prompt: Can you do a histogram plot on the GitHub events against time Pinot MCP fetching data and analyzing table

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 git@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.

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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选