Data Visualization MCP Server

by markomitranic
Verified
# Generated by https://smithery.ai. See: https://smithery.ai/docs/config#dockerfile # Use a Python image with uv pre-installed FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS uv # Install the project into /app WORKDIR /app # Enable bytecode compilation ENV UV_COMPILE_BYTECODE=1 # Copy from the cache instead of linking since it's a mounted volume ENV UV_LINK_MODE=copy # Copy the project files first so we have access to the lockfile COPY pyproject.toml uv.lock ./ # Install the project's dependencies using the lockfile and settings RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-install-project --no-dev --no-editable # Then, add the rest of the project source code and install it # Installing separately from its dependencies allows optimal layer caching ADD . /app RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-dev --no-editable FROM python:3.12-slim-bookworm WORKDIR /app # Copy the virtual environment from the build stage COPY --from=uv /app/.venv /app/.venv # Copy the uv executable from the build stage COPY --from=uv /usr/local/bin/uv /usr/local/bin/uv # Create logs directory RUN mkdir -p logs # Place executables in the environment at the front of the path ENV PATH="/app/.venv/bin:$PATH" # when running the container, add --output-type and a bind mount to the host's db file ENTRYPOINT ["uv", "run", "mcp_server_vegalite"]