fleetanalytics_mcp/Dockerfile

# fleetanalytics-mcp — read-only Fleet Analytics MCP server.
# Coolify auto-detects this Dockerfile: set the app port to 8892, attach the
# domain (e.g. fleetmcp.rahamafresh.com) in the Coolify UI, set DATABASE_URL
# (analytics_ro DSN) + MCP_AUTH_TOKENS as secrets, and connect the app to the
# network that can reach timescale_db. See README.md / docs/ANALYTICS_MCP.md.
FROM python:3.12-slim

# uv for fast, reproducible dependency installs.
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/

WORKDIR /app

# Install ONLY dependencies (flat module — the project itself is not a package).
# Copy the lockfile and build --frozen so rebuilds are reproducible: without it,
# `uv sync` re-resolves the >= ranges in pyproject.toml and a redeploy could pull a
# newer, behaviour-changed mcp/starlette and break the running server.
COPY pyproject.toml uv.lock ./
RUN uv sync --no-dev --no-install-project --frozen
ENV PATH="/app/.venv/bin:$PATH"

COPY analytics_mcp.py ./

# Run as a non-root user (least privilege; nothing here needs root).
RUN useradd -m -u 10001 app && chown -R app:app /app
USER app

EXPOSE 8892

HEALTHCHECK --interval=30s --timeout=3s --start-period=10s --retries=3 \
  CMD python -c "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:8892/healthz').status==200 else 1)" || exit 1

# Single worker: this is a low-traffic read-only proxy for a handful of analysts, and
# the DB connection budget = workers × MCP_POOL_MAX. One worker (× default pool 8) caps
# the MCP at 8 backends instead of 16, which matters on a shared 100-connection DB.
# Scale out by raising MCP_POOL_MAX, not workers, so the budget stays obvious.
CMD ["uvicorn", "analytics_mcp:app", "--host", "0.0.0.0", "--port", "8892", "--workers", "1"]