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James-Chahwan/repo-graph

by Various

Structural graph memory for AI coding assistants — MCP server for codebase navigation

J

MCP

James-Chahwan/repo-graph

Added 1 June 2026

Overview

An MCP server written in Rust that provides structural graph memory for AI coding assistants. It models a codebase as a graph of files, functions, and relationships to enable context-aware navigation during code generation and analysis.

Best for

Best for
Developers using MCP-compatible AI coding assistants who need structured context from their codebase.

Use cases

  • Give an AI coding assistant a structured graph of the entire codebase
  • Improve code generation accuracy by supplying relevant code relationships
  • Navigate large or unfamiliar repositories quickly through graph queries

How to use

Install

uvx mcp-repo-graph install

Tools exposed

  • repo-graph

Tested with

VS Code, VS Code Insiders, Cursor

Notes

An MCP server written in Rust that provides structural graph memory for AI coding assistants. It models a codebase as a graph of files, functions, and relationships to enable context-aware navigation during code generation and analysis.

5 stars on GitHub. Last updated 2026-05-22. Licensed MIT.

Use cases

  • Give an AI coding assistant a structured graph of the entire codebase
  • Improve code generation accuracy by supplying relevant code relationships
  • Navigate large or unfamiliar repositories quickly through graph queries

Pros

  • Lightweight and performant due to Rust implementation
  • Integrates via the standard Model Context Protocol
  • Open source and free to use

Cons

  • Very early stage with minimal adoption (only 5 stars on GitHub)
  • Limited documentation and community support
  • Unclear compatibility with languages beyond the tool’s own Rust parsing

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight and performant due to Rust implementation
  • Integrates via the standard Model Context Protocol
  • Open source and free to use

Cons

  • Very early stage with minimal adoption (only 5 stars on GitHub)
  • Limited documentation and community support
  • Unclear compatibility with languages beyond the tool's own Rust parsing
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