conda-cli-mcp

conda-cli-mcp exposes an installed conda command line through the Model Context Protocol. It discovers built-in and plugin-contributed commands at startup, creates structured tools from conda's argparse definitions, and keeps the complete CLI reachable through an explicitly enabled raw argv tool.

The server runs conda in bounded child processes. Conda remains responsible for configuration, plugin ordering, solving, transactions, reporters, authentication, hooks, and error handling.

When conda-completion 0.3 or newer is installed in the target environment, a read-only metadata tool exposes its command-specific completion hints through the plugin's public Python API.

The project is alpha software. It requires Python 3.10 or newer and supports target conda versions >=26.7,<27.

Installation

Install the published command into an isolated environment:

pipx install conda-cli-mcp

Keep the server environment separate from the target conda installation and select that installation with --conda-exe.

Related MCP server: AWS MCP Server

Documentation

The documentation is organized by purpose:

• Tutorial

• How-to guides

• Reference

• Explanation

• Changelog

Documentation is published at jezdez.github.io/conda-cli-mcp.

Safety

The default policy is read-only. --allow-write permits conda-managed mutations. --allow-exec permits conda run, opaque passthrough arguments, and the raw conda_execute tool. Policy is fixed at startup and enforced before conda starts.

Running the server

Select the target conda executable explicitly when it lives in another environment:

conda-cli-mcp --conda-exe /absolute/path/to/conda

Without --conda-exe, resolution checks CONDA_EXE and then PATH. MCP clients connect over standard input and standard output. See the getting-started tutorial for a client configuration.

Project scope

• conda-cli-mcp owns local conda CLI discovery and execution.

• conda-meta-mcp owns read-only package and ecosystem metadata.

• conda-mcp is reserved for a future umbrella package that installs both.

Development

Development, documentation, and packaging use Pixi:

pixi install
pixi run test
pixi run check
pixi run -e docs docs
pixi run -e build package

The conda recipe is noarch Python and lives in recipe/recipe.yaml.