Smithery CLI

Smithery CLI – A Unified MCP Server Manager

Smithery CLI is a command‑line tool that bridges Model Context Protocol (MCP) servers with AI assistants in a single, client‑agnostic workflow. It solves the friction that developers face when installing, configuring, and running MCP servers for different AI platforms such as Claude or OpenAI. By providing a consistent interface, the CLI eliminates the need to remember platform‑specific commands or scripts and lets teams manage all MCP servers from one place.

At its core, the CLI offers a set of high‑level commands that cover every stage of an MCP server’s lifecycle. Install and uninstall let you add or remove servers from the Smithery registry, automatically handling client selection and configuration prompts. Run launches a server instance with optional JSON‑encoded settings, while build packages the server for production deployment using either standard I/O or secure HTTP transport. The inspect command opens an interactive console to test server responses before integration, and playground launches a web interface that mimics the AI client’s prompt/response loop. These commands are designed to be intuitive for developers who already work with npm or Node.js, making the learning curve minimal.

The CLI’s key capabilities include:

• Client‑agnostic management – One command set works for any MCP‑compatible AI assistant, so teams can switch or add clients without rewriting scripts.
• Interactive configuration – When installing a server, the CLI prompts for missing data or lets you provide a JSON payload to skip steps.
• Hot‑reload development – The dev command starts a live server with automatic recompilation and an optional tunnel, speeding up iteration cycles.
• Secure key handling – The login command stores an API key in a local credential store, ensuring that sensitive tokens are not exposed on the command line.
• Extensible registry – Servers can be searched and inspected directly from the Smithery registry, encouraging reuse of community‑built MCP implementations.

Typical use cases include:

• Rapid prototyping – A developer can spin up a new MCP server, test it in the playground, and integrate it into an AI assistant with just a few CLI commands.
• Continuous integration – CI pipelines can install, build, and run MCP servers automatically, ensuring that all environments are identical.
• Multi‑client deployments – Organizations using both Claude and OpenAI can maintain separate server configurations while keeping the same tooling.
• Educational demos – Instructors can provide students with a single command to install and run a variety of MCP servers, focusing on learning rather than setup.

By abstracting away the boilerplate and providing a unified command set, Smithery CLI enables developers to focus on building powerful AI experiences rather than wrestling with server management details. Its integration with the Smithery registry, interactive prompts, and dev‑friendly features make it a standout tool for any team looking to streamline MCP server workflows.