MCP-Use

MCP‑Use is a TypeScript framework that turns the Model Context Protocol into a developer‑friendly platform for building intelligent AI agents, creating custom MCP servers, and debugging them with an integrated inspector.
At its core, the framework solves a common pain point for AI developers: wiring together language models, external tools, and user interfaces without reinventing the wheel. By exposing a consistent API for both clients and servers, MCP‑Use lets teams focus on business logic rather than protocol plumbing. The result is a modular stack that can be dropped into any TypeScript project and scaled from local prototypes to production‑grade services.

The server component is built on top of the MCP specification and bundles a suite of ready‑made tools—filesystem access, GitHub integration, and more—each exposed as a lightweight sub‑server. Developers can spin up these tools with a single command and then expose them to LLMs via the . The framework also ships a web‑based inspector that visualizes request/response flows, logs, and tool usage in real time. This live debugging view dramatically reduces the iteration cycle for complex agent workflows, making it easier to spot mis‑behaviors or performance bottlenecks.

Key capabilities include:

• Unified tool registry: register any external service as an MCP server and make it instantly available to agents.
• Hot‑reload development: the CLI automatically restarts the server and refreshes the inspector whenever code changes, keeping the feedback loop tight.
• Rich UI widgets: build custom dashboards or command panels that interact directly with MCP endpoints, enabling seamless user experiences.
• TypeScript safety: all APIs are strongly typed, reducing runtime errors and improving developer ergonomics.

Real‑world use cases span from building a code‑review assistant that pulls repository data via the GitHub server, to creating a knowledge‑base chatbot that queries local files through the filesystem tool. In enterprise settings, teams can expose internal APIs as MCP servers and let LLMs orchestrate them, unlocking powerful automation without exposing raw credentials.

By integrating MCP‑Use into existing AI workflows, developers gain a single, coherent ecosystem that handles the heavy lifting of protocol management, tool orchestration, and debugging. The result is faster prototyping, cleaner codebases, and more reliable AI agents that can seamlessly interact with the world around them.