mle_kit_mcp

# MLE kit MCP [](https://pypi.org/project/mle-kit-mcp/) [](https://github.com/IlyaGusev/mle_kit_mcp/actions/workflows/python.yml) [](LICENSE) MCP server providing practical tools for ML engineering workflows, including local/remote bash, a text editor, file search, remote GPU helpers (via vast.ai), and an OpenRouter LLM proxy. ### Features - **bash**: Run commands in an isolated Docker container mounted to your `WORKSPACE_DIR`. - **text_editor**: View and edit files and directories in your workspace with undo support. - **glob / grep**: Fast filename globbing and ripgrep-based content search. - **remote_bash / remote_text_editor / remote_download**: Execute and edit on a remote GPU machine and sync files to/from it. - **llm_proxy_local / llm_proxy_remote**: Launch an OpenAI-compatible proxy backed by OpenRouter locally (in the bash container) or on the remote GPU. ## Requirements - Python 3.12+ - Docker daemon available (for `bash` tool) - ripgrep (`rg`) installed on the host (for `grep` tool) - `WORKSPACE_DIR` should be set with a path to working directory - Optional (for remote GPU tools): a `VAST_AI_KEY` with billing set up on vast.ai - Optional (for LLM proxy tools): an `OPENROUTER_API_KEY` ## Install Using uv (recommended): ```bash uv sync ``` Or standard pip install: ```bash python -m venv .venv && . .venv/bin/activate pip install -e . ``` ## Run the MCP server Set a workspace directory and start the server. The MCP endpoint is served at `/mcp`. ```bash WORKSPACE_DIR=/absolute/path/to/workdir uv run python -m mle_kit_mcp --port 5057 ``` Defaults: - `PORT` defaults to `5057` if `--port` is not provided - `mount_path=/` and `streamable_http_path=/mcp` ### Claude Desktop config ```json { "mcpServers": { "mle_kit": { "command": "python3", "args": [ "-m", "mle_kit_mcp", "--transport", "stdio" ] } } } ``` ## Tools overview - **bash(command, cwd=None, timeout=60)**: Runs inside a `python:3.12-slim` container with your workspace bind-mounted at `/workdir`. State persists between calls. Timeouts return a helpful message. - **text_editor(command, path, ...)**: Supports `view`, `write`, `append`, `insert`, `str_replace` (with optional `dry_run`), and `undo_edit`. Only relative paths under the workspace are allowed. - **glob(pattern, path=None)**: Returns matching files under the workspace (optionally under `path`), sorted by modification time. - **grep(pattern, path=None, glob=None, output_mode=..., ...)**: ripgrep wrapper. Install `rg` on the host to enable. Output modes: `files_with_matches`, `content`, `count`. - **remote_bash(command, timeout=60)**: Runs commands on a remote vast.ai instance. Manages lifecycle unless you supply an existing instance (see env vars below). - **remote_download(file_path)**: Copies a file from the remote (`/root/<file_path>`) to your workspace. - **remote_text_editor(...)**: Same API as `text_editor`, but syncs the file(s) before and after edits to the remote. - **llm_proxy_local() / llm_proxy_remote()**: Starts a small FastAPI OpenAI-compatible server backed by OpenRouter, returning a JSON string with `url` and `scope`. ## Configuration (env vars) All variables can be placed in a local `.env` file or exported in your shell. - `WORKSPACE_DIR` (required): Absolute path to your workspace directory. - `PORT` (optional): Default server port (defaults to `5057`). Remote GPU (vast.ai): - `GPU_TYPE` (default: `RTX_3090`) - `DISK_SPACE` (GB, default: `300`) - `EXISTING_INSTANCE_ID` (optional): Use an existing vast.ai instance instead of creating a new one. - `EXISTING_SSH_KEY` (optional): Path to an SSH private key to use with the existing instance. - `VAST_AI_KEY` (optional but required to launch new instances) OpenRouter proxy: - `OPENROUTER_API_KEY` (optional but required for proxy tools) - `OPENROUTER_BASE_URL` (default: `https://openrouter.ai/api/v1`) Notes: - The remote GPU helper will generate an SSH key at `~/.ssh/id_rsa` if one is missing, and attach it to the instance. - Creating/destroying instances may incur cost; be mindful of environment defaults. ## Development Run tests: ```bash make test ``` Lint / type-check / format: ```bash make validate ``` ## Docker You can also build and run via the provided `Dockerfile`: ```bash docker build -t mle_kit_mcp . docker run --rm -p 5057:5057 \ -e PORT=5057 \ -e WORKSPACE_DIR=/workspace \ -v "$PWD/workdir:/workspace" \ mle_kit_mcp ```