@yuqiaohan95
Basic information
MCP server for intelligent code context — 87%+ token savings, zero resource overhead.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"graph-context": {
"command": "python",
"args": [
"-m",
"tests.experiment"
]
}
}
}Tool list not available yet
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Graph Context is an MCP server that retrieves precise code context using AST call graphs and BM25 ranking, designed for AI coding assistants. It runs pure Python with no GPU or external services, delivering token savings of 87%+ for single-agent and multi-agent workflows.
Install with pip install graph-context (or graph-context[full] for Chinese tokenization and file watching). Run as an MCP server with the command graph-context, optionally setting environment variables like PROJECT_ROOT, MCP_MAX_TOKENS, and MCP_TOP_K. Integrate with Claude Desktop, Cursor, or Cline via a JSON configuration pointing to the command. Programmatic usage is also available via the VibeCodingEngine class.
Graph Context requires no vector DB deployment, no GPU for embeddings, no external API calls, and no large memory footprint. It uses lightweight BM25 inverted indices and AST‑level exact matching instead of semantic similarity.
Pure Python — no GPU, no external services. The `full` install adds Chinese tokenization and file‑watching dependencies. Index persistence and rules are stored as local JSON files (configurable paths).
All data stays local on your machine. Index persistence is optional; by default the index is built in memory. Rules and scopes are saved to JSON files on disk (paths controlled by `MCP_RULES_PATH` and `MCP_SCOPES_PATH`).
It runs as a local MCP server over stdio transport, started via the `graph-context` command. No authentication is required because it operates entirely on the local machine.
Benchmarks show 100% recall on small projects and up to 97% recall on large projects when using the self‑evolving rules engine. Token savings range from 76% to 91% depending on project size and whether rules are applied.
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