> ## Documentation Index
> Fetch the complete documentation index at: https://fastmcp-docs-v3-beta2-feature-tracking.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Generate CLI

> Turn any MCP server into a standalone, typed command-line tool.

export const VersionBadge = ({version}) => {
  return <Badge stroke size="lg" icon="gift" iconType="regular" className="version-badge">
            New in version <code>{version}</code>
        </Badge>;
};

<VersionBadge version="3.0.0" />

`fastmcp list` and `fastmcp call` let you poke at a server interactively, but they're developer tools — you always have to spell out the server spec, the tool name, and the arguments. `fastmcp generate-cli` takes the next step: it connects to a server, reads its schemas, and writes a standalone Python script where every tool is a proper subcommand with typed flags, help text, and tab completion. The result is a CLI that feels like it was hand-written for that specific server.

The key insight is that MCP tool schemas already contain everything a CLI framework needs: parameter names, types, descriptions, required/optional status, and defaults. `generate-cli` maps that schema into [cyclopts](https://cyclopts.readthedocs.io/) commands, so JSON Schema types become Python type annotations, descriptions become `--help` text, and required parameters become mandatory flags.

## Generating a Script

Point the command at any server spec — URLs, Python files, discovered server names, MCPConfig JSON — and it writes a CLI script:

```bash theme={null}
fastmcp generate-cli weather
fastmcp generate-cli http://localhost:8000/mcp
fastmcp generate-cli server.py my_weather_cli.py
```

The second positional argument sets the output path. When omitted, it defaults to `cli.py`. If the file already exists, the command refuses to overwrite unless you pass `-f`:

```bash theme={null}
fastmcp generate-cli weather -f
fastmcp generate-cli weather my_cli.py -f
```

Name-based resolution works here too, so if you have a server configured in Claude Desktop, Cursor, or any other supported editor, you can reference it by name. Run [`fastmcp discover`](/clients/cli#discovering-configured-servers) to see what's available.

```bash theme={null}
fastmcp generate-cli claude-code:my-server output.py
```

The `--timeout` and `--auth` flags work the same way they do in `fastmcp list` and `fastmcp call`.

## What You Get

The generated script is a regular Python file — executable, editable, and yours. Here's what it looks like in practice:

```
$ python cli.py --help
Usage: weather-cli COMMAND

CLI for weather MCP server

Commands:
  call-tool       Call a tool on the server
  list-tools      List available tools.
  list-resources  List available resources.
  read-resource   Read a resource by URI.
  list-prompts    List available prompts.
  get-prompt      Get a prompt by name. Pass arguments as key=value pairs.
```

The `call-tool` subcommand is where the generated code lives. Each tool on the server becomes its own command:

```
$ python cli.py call-tool --help
Usage: weather-cli call-tool COMMAND

Call a tool on the server

Commands:
  get_forecast  Get the weather forecast for a city.
  search_city   Search for a city by name.
```

And each tool has typed parameters with help text pulled directly from the server's schema:

```
$ python cli.py call-tool get_forecast --help
Usage: weather-cli call-tool get_forecast [OPTIONS]

Get the weather forecast for a city.

Options:
  --city    [str]  City name (required)
  --days    [int]  Number of forecast days (default: 3)
```

Tool names are preserved exactly as the server defines them — underscores stay as underscores, so `call-tool get_forecast` matches what the server expects.

## How It Works

The generated script is a client, not a server. It doesn't bundle or embed the MCP server — it connects to it on every invocation. For URL-based servers, the server needs to be running. For stdio-based servers, the command specified in `CLIENT_SPEC` must be available on the system's `PATH`.

At the top of the generated file, a `CLIENT_SPEC` variable holds the resolved transport: either a URL string or a `StdioTransport` with the command and arguments baked in. Every invocation connects through this spec, so the script works without any external configuration.

### Parameter Handling

Parameters are mapped intelligently based on their complexity:

**Simple types** (`string`, `integer`, `number`, `boolean`) become typed Python parameters with clean flags:

```bash theme={null}
python cli.py call-tool get_forecast --city London --days 3
```

**Arrays of simple types** (`array` with `string`/`integer`/`number`/`boolean` items) become `list[T]` parameters that accept multiple flags:

```bash theme={null}
python cli.py call-tool tag_items --tags python --tags fastapi --tags mcp
```

**Complex types** (objects, nested arrays, or unions) accept JSON strings. The tool's `--help` displays the full JSON schema so you know exactly what structure to pass:

```bash theme={null}
python cli.py call-tool create_user \
  --name John \
  --metadata '{"role": "admin", "dept": "engineering"}'
```

Required parameters are mandatory flags; optional ones default to their schema default or `None`. Empty values are filtered out before calling the server.

Beyond tool commands, the script includes generic commands that work regardless of what the server exposes: `list-tools`, `list-resources`, `read-resource`, `list-prompts`, and `get-prompt`. These connect to the server at runtime, so they always reflect the server's current state even if the tools have changed since generation.

## Editing the Output

The most common edit is changing `CLIENT_SPEC`. If you generated from a local dev server and want to point at production, just change the string. If you generated from a discovered name and want to pin the transport, replace it with an explicit URL or `StdioTransport`.

Beyond that, it's a regular Python file. You can add commands, change the output formatting, integrate it into a larger application, or strip out the parts you don't need. The helper functions (`_call_tool`, `_print_tool_result`) are thin wrappers around `fastmcp.Client` that are easy to adapt.

The generated script requires `fastmcp` as a dependency. If the script lives outside a project that already has fastmcp installed, `uv run` is the easiest way to run it without permanent installation:

```bash theme={null}
uv run --with fastmcp python cli.py call-tool get_forecast --city London
```