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The CLI can act as an MCP client — connecting to any server (local or remote) to list what it exposes and call its tools directly. This is useful for development, debugging, scripting, and giving shell-capable LLM agents access to MCP servers.

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Listing Tools

fastmcp list connects to a server and prints its tools as function signatures, showing parameter names, types, and descriptions at a glance:
When you need the full JSON Schema for a tool’s inputs or outputs — for understanding nested objects, enum constraints, or complex types — opt in with --input-schema or --output-schema:

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Resources and Prompts

By default, only tools are shown. Add --resources or --prompts to include those:

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Machine-Readable Output

The --json flag switches to structured JSON with full schemas included. This is the format to use when feeding tool definitions to an LLM or building automation:

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Options

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Calling Tools

fastmcp call invokes a single tool on a server. Pass arguments as key=value pairs — the CLI fetches the tool’s schema and coerces your string values to the right types automatically:
Type coercion is schema-driven: "5" becomes the integer 5 when the schema expects an integer. Booleans accept true/false, yes/no, and 1/0. Arrays and objects are parsed as JSON.

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Complex Arguments

For tools with nested or structured parameters, key=value syntax gets awkward. Pass a single JSON object instead:
Or use --input-json to provide a base dictionary, then override individual keys with key=value pairs:

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Error Handling

If you misspell a tool name, the CLI suggests corrections via fuzzy matching. Missing required arguments produce a clear message with the tool’s signature as a reminder. Tool execution errors are printed with a non-zero exit code, making the CLI straightforward to use in scripts.

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Structured Output

--json emits the raw result including content blocks, error status, and structured content:

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Interactive Elicitation

Some tools request additional input during execution through MCP’s elicitation mechanism. When this happens, the CLI prompts you in the terminal — showing each field’s name, type, and whether it’s required. You can type decline to skip a question or cancel to abort the call entirely.

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Options

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Discovering Configured Servers

fastmcp discover scans your machine for MCP servers configured in editors and tools. It checks:
  • Claude Desktop — claude_desktop_config.json
  • Claude Code — ~/.claude.json
  • Cursor — .cursor/mcp.json (walks up from current directory)
  • Gemini CLI — ~/.gemini/settings.json
  • Goose — ~/.config/goose/config.yaml
  • Project — ./mcp.json in the current directory
The output groups servers by source, showing each server’s name and transport. Filter by source or get machine-readable output:
Any server that appears here can be used by name with list, call, and other commands — so you can go from “I have a server in Claude Code” to querying it without copying URLs or paths.

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LLM Agent Integration

For LLM agents that can execute shell commands but don’t have native MCP support, the CLI provides a clean bridge. The agent calls fastmcp list --json to discover available tools with full schemas, then fastmcp call --json to invoke them with structured results. Because the CLI handles connection management, transport selection, and type coercion internally, the agent doesn’t need to understand MCP protocol details — it just reads JSON and constructs shell commands.

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Remote Stdio Bridges

For MCP hosts that expect a local stdio command but need to connect to a remote HTTP server, use fastmcp-remote. It provides a small standalone bridge for host configuration, while fastmcp list and fastmcp call remain focused on direct inspection and invocation from the terminal.