mcp-cli

The mcp-cli tool is a lightweight command‑line inspector designed to simplify interaction with Model Context Protocol (MCP) servers. It addresses the common pain point of manually navigating an MCP server’s API surface by providing a unified interface that can launch servers, list their capabilities, and invoke primitives—all from the terminal. For developers who work with AI assistants such as Claude, this means a single point of entry to discover and test the tools, resources, and prompts that an MCP server exposes, without having to write custom client code.

At its core, mcp-cli can start an MCP server from a variety of sources: local files, npm packages, or pre‑existing binaries. Once running, it automatically queries the server’s introspection endpoints to enumerate available tools, resources, and prompts. These listings are presented in a clear, human‑readable format, allowing developers to quickly see what actions can be performed (e.g., ), what data can be fetched (), or what prompts are ready for reuse. The ability to read resources and prompts directly from the CLI removes the need to manually construct HTTP requests or parse JSON responses.

The tool also supports direct invocation of primitives in two modes. In interactive mode, users can trigger a tool or prompt and provide arguments via prompts on the command line. In non‑interactive mode, developers can script calls by specifying the target and JSON arguments inline—ideal for CI pipelines or automated workflows. This dual approach ensures that both exploratory testing and production automation can be handled with the same command set.

Security and integration are further enhanced by built‑in OAuth support for both Server‑Sent Events (SSE) and Streamable HTTP servers. This allows mcp-cli to authenticate against protected MCP endpoints, cache tokens, and even purge stored credentials when needed. The command provides a quick way to clear sensitive data from local storage, keeping development environments clean and secure.

In practice, mcp-cli shines in scenarios where AI assistants need to interact with external systems—such as reading files, querying databases, or invoking custom microservices. By exposing these capabilities through a consistent CLI interface, developers can prototype assistant workflows, validate server functionality, and integrate MCP servers into larger automation pipelines with minimal friction.