Cgraphy MCP server

cgraphy – Code Knowledge Graph MCP Server

Paper (DOI): https://doi.org/10.5281/zenodo.21422935 PyPI: https://pypi.org/project/cgraphy/ GitHub: https://github.com/pmgarg/cgraphy

cgraphy is a Python code knowledge graph and Model Context Protocol (MCP) server for AI coding agents such as Claude Code, Codex CLI, Cursor, and Gemini CLI.

cgraphy indexes any codebase into a knowledge graph — functions, classes and files as nodes; calls, imports, inheritance and git co-change history as edges — and serves compact, token-budgeted slices of it to AI assistants through the Model Context Protocol. Instead of re-reading dozens of files to orient itself on every prompt, an agent asks the graph and gets the relevant subgraph in a couple of thousand tokens.

Python, TypeScript/JavaScript, Java, Go, C, C++ and Rust; generic definition-level extraction for 20+ more via tree-sitter; config and docs files participate through summaries.

• Any language. Full-fidelity extraction (calls, imports, inheritance) for

symbols first in every answer.

• Importance-ranked. PageRank over the code graph puts load-bearing

symbol and stops exactly at your token budget — cost scales with the question, not the repo.

• Token-budgeted. cgraphy_context expands the graph greedily around a

together (logical coupling), an edge type static analysis can't see.

• Git-aware. --git-history mines commit history for files that change

through the enrich loop; summaries survive re-indexing via content hashing.

• No API key. Semantic summaries are written by the host agent itself

daemons, no vector database.

• Zero infrastructure. One SQLite file in .cgraphy/. No services, no

the deployed configuration resolved 14 vs 8 of 57 real GitHub issues (official Docker harness). Indexes kubernetes (26K files, 219K nodes) in 49s, keeps it fresh in 1.6s cycles, answers queries in 1–152ms. All benchmarks and predictions are in this repo (Research paper and benchmarks).

• Proven end-to-end. In 400+ controlled agent runs on SWE-bench Lite,

Install

pip install cgraphy        # or: uv tool install cgraphy

Quick start

cd your-repo
cgraphy init          # one command: MCP config + agent steering + index

cgraphy init does three things:

Code** in all its forms: CLI, VSCode extension, and the desktop app.

1. Writes a project-scoped .mcp.json — picked up automatically by **Claude

consult the graph (cgraphyoverview → cgraphysearch → cgraphycontext) before* reading files — this is what makes the graph actually replace bulk file reading. (Agents can't be forced, only steered: instruction files + persuasive tool descriptions + the tools being genuinely faster is the mechanism, and it works.)

1. Appends a steering block to CLAUDE.md and AGENTS.md telling agents to

1. Builds the index with git co-change history.

Or register the MCP server manually with your assistant:

Claude Code

claude mcp add cgraphy -- uvx cgraphy serve /path/to/repo

Codex CLI (~/.codex/config.toml)

[mcp_servers.cgraphy]
command = "uvx"
args = ["cgraphy", "serve", "/path/to/repo"]

Gemini CLI (~/.gemini/settings.json) / Cursor (.cursor/mcp.json)

Shortened. The full README is on GitHub.