TanStack MCP Server

Overview

The MCP TanStack Example server demonstrates how to expose a Model Context Protocol (MCP) endpoint that integrates seamlessly with the popular TanStack ecosystem. By running a lightweight Node.js application, developers can quickly spin up an MCP server that serves as a bridge between AI assistants—such as Claude—and the rich tooling and data sources provided by TanStack. This setup eliminates the need for custom adapters or manual API plumbing, allowing AI assistants to invoke TanStack utilities directly through MCP commands.

The core problem this server solves is the friction developers face when connecting AI assistants to existing JavaScript/TypeScript tooling. Traditional approaches require writing bespoke HTTP handlers, managing authentication, and translating between the assistant’s expectations and the tool’s API. MCP TanStack Example abstracts all of that complexity behind a single, well‑defined endpoint (). The server listens for SSE (Server‑Sent Events) streams, interprets MCP requests, and dispatches them to TanStack functions. This means an AI assistant can issue a command like and immediately interact with TanStack’s powerful data fetching, caching, or state‑management capabilities without any additional boilerplate.

Key features of this MCP server include:

• Zero‑configuration transport: Built on top of Vercel’s proven MCP adapter, the server uses the same transport layer that powers production deployments. This guarantees consistent behavior across local development and cloud hosting.
• Command‑based invocation: The server accepts simple command strings () that can be embedded directly in AI prompts. This keeps the interaction surface minimal and intuitive for end users.
• SSE‑driven streaming: By leveraging Server‑Sent Events, the server can stream incremental responses back to the AI assistant. This is especially useful for long‑running data queries or continuous updates from TanStack’s observables.
• TypeScript friendliness: The example is written in TypeScript, ensuring type safety and IDE support for developers accustomed to the TanStack stack.

Typical use cases include:

• Data‑driven AI assistants: A Claude instance can fetch live data from a TanStack query, cache results, and present them in natural language responses.
• Real‑time collaboration tools: Integrate TanStack’s state management to keep AI‑generated UI components in sync with user actions.
• Rapid prototyping: Quickly expose custom TanStack utilities to an AI assistant for testing or demonstration purposes without building a full REST API.

By embedding MCP TanStack Example into your development workflow, you gain a plug‑and‑play bridge that turns any TanStack tool into an AI‑friendly endpoint. The result is a smoother, more productive collaboration between human developers and AI assistants, unlocking new possibilities for intelligent tooling and automated workflows.