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58 lines
1.7 KiB
Docker
58 lines
1.7 KiB
Docker
# Skill Seekers MCP Server - Docker Image
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# Optimized for MCP server deployment (stdio + HTTP modes)
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FROM python:3.12-slim
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LABEL maintainer="Skill Seekers <noreply@skillseekers.dev>"
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LABEL description="Skill Seekers MCP Server - 35 tools for AI skills generation"
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LABEL version="3.3.0"
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WORKDIR /app
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# Install runtime dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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git \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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# Create non-root user
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RUN useradd -m -u 1000 -s /bin/bash mcp && \
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mkdir -p /app /data /configs /output && \
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chown -R mcp:mcp /app /data /configs /output
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# Copy application files
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COPY --chown=mcp:mcp src/ src/
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COPY --chown=mcp:mcp configs/ configs/
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COPY --chown=mcp:mcp pyproject.toml README.md ./
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# Install dependencies
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RUN pip install --no-cache-dir --upgrade pip && \
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pip install --no-cache-dir -e ".[all-llms]" && \
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pip install --no-cache-dir mcp
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# Switch to non-root user
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USER mcp
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# Environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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MCP_TRANSPORT=http \
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MCP_PORT=8765 \
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SKILL_SEEKERS_HOME=/data \
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SKILL_SEEKERS_OUTPUT=/output
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# Health check for HTTP mode
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HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \
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CMD curl -f http://localhost:${MCP_PORT}/health || exit 1
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# Volumes
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VOLUME ["/data", "/configs", "/output"]
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# Expose MCP server port (default 8765, overridden by $PORT on cloud platforms)
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EXPOSE ${MCP_PORT:-8765}
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# Start MCP server in HTTP mode by default
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# Uses shell form so $PORT/$MCP_PORT env vars are expanded at runtime
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# Cloud platforms (Render, Railway, etc.) set $PORT automatically
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CMD python -m skill_seekers.mcp.server_fastmcp --http --host 0.0.0.0 --port ${PORT:-${MCP_PORT:-8765}}
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