Pinot MCP Server

The Apache Pinot MCP server bridges the gap between large‑scale analytical data stores and conversational AI assistants. It exposes a lightweight Python API that lets Claude Desktop query an Apache Pinot cluster directly from the chat interface, turning static dashboards into interactive, AI‑powered data exploration tools. By running as a standard MCP server, it can be launched locally or in the cloud and integrated into any workflow that supports the Model Context Protocol.

At its core, the server provides a set of read‑only tools that mirror common Pinot operations: listing tables and segments, retrieving schema metadata, and executing SQL queries. These capabilities are wrapped in a JSON‑based protocol that Claude understands, so users can ask high‑level questions like “Show me a histogram of GitHub events over time” and the assistant will automatically translate that into a Pinot query, fetch the results, and render a visual output. This eliminates the need for developers or analysts to write SQL themselves, reducing friction and accelerating insight generation.

Key features include:

• Metadata discovery – Quickly enumerate tables, columns, and index structures to understand the shape of the data without consulting external documentation.
• Query execution – Execute arbitrary read‑only SQL against the cluster, with built‑in safeguards that prevent data modification.
• Visualization support – Claude can request simple plots (e.g., histograms, line charts) that are generated from query results and returned inline in the chat.
• Seamless integration – The server can be added to Claude Desktop’s MCP configuration with a single JSON entry, making it available across all conversations.
• Rapid prototyping – Developers can spin up a Pinot QuickStart instance in Docker and immediately test the MCP server against a realistic dataset, enabling end‑to‑end validation of queries.

Real‑world scenarios where this server shines include business intelligence teams that need to answer ad‑hoc questions on streaming data, product managers who want instant visual feedback on usage metrics, and data scientists who wish to prototype analytical pipelines without leaving their conversational environment. By treating Pinot as a first‑class tool in the AI assistant’s repertoire, the MCP server empowers teams to iterate faster, reduce context switching, and democratize access to complex analytical queries.