Dockerfile.mcp

# Skill Seekers MCP Server - Docker Image
# Optimized for MCP server deployment (stdio + HTTP modes)

FROM python:3.12-slim

LABEL maintainer="Skill Seekers <noreply@skillseekers.dev>"
LABEL description="Skill Seekers MCP Server - 40 tools for AI skills generation"
LABEL version="3.9.0.dev0"

WORKDIR /app

# Install runtime dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Create non-root user
RUN useradd -m -u 1000 -s /bin/bash mcp && \
    mkdir -p /app /data /configs /output && \
    chown -R mcp:mcp /app /data /configs /output

# Copy application files
COPY --chown=mcp:mcp src/ src/
COPY --chown=mcp:mcp configs/ configs/
COPY --chown=mcp:mcp pyproject.toml README.md ./

# Install dependencies
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -e ".[all-llms]" && \
    pip install --no-cache-dir mcp

# Switch to non-root user
USER mcp

# Environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    MCP_TRANSPORT=http \
    MCP_PORT=8765 \
    SKILL_SEEKERS_HOME=/data \
    SKILL_SEEKERS_OUTPUT=/output

# Health check for HTTP mode
HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \
    CMD curl -f http://localhost:${MCP_PORT}/health || exit 1

# Volumes
VOLUME ["/data", "/configs", "/output"]

# Expose MCP server port (default 8765, overridden by $PORT on cloud platforms)
EXPOSE ${MCP_PORT:-8765}

# Start MCP server in HTTP mode by default
# Uses shell form so $PORT/$MCP_PORT env vars are expanded at runtime
# Cloud platforms (Render, Railway, etc.) set $PORT automatically
CMD python -m skill_seekers.mcp.server_fastmcp --http --host 0.0.0.0 --port ${PORT:-${MCP_PORT:-8765}}