Context engineering platform for AI coding agents with graph-aware retrieval, repository intelligence, MCP integration, and evaluation.
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ContextOS is an MCP server that context engineering platform for AI coding agents with graph-aware retrieval, repository intelligence, MCP integration, and evaluation. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 67/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"contextos": {
"env": {
"CONTEXTOS_DATABASE_URL": "postgresql://you@localhost:5432/contextos",
"CONTEXTOS_MCP_ALLOWED_REPOS": "ky,itsdangerous"
},
"args": [
"-m",
"contextos.mcp_server"
],
"command": "/path/to/ContextOS/backend/.venv/bin/python"
}
}
}Are you the author?
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Context engineering and retrieval for AI coding agents. ContextOS indexes code repositories and their documentation, traces how files and symbols depend on each other, and assembles the smallest, cited, trustworthy context package a coding agent needs for a specific task. Agents consume it through an MCP server; people inspect and tune it in a web studio.
No automated test available for this server. Check the GitHub README for setup instructions.
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
Click any tool to inspect its schema.
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