mcp-repo-graph 0.1.1

MCP server that gives LLMs a structural map of any codebase — entities, relationships, and flows — so they navigate to the right files without reading everything first.

repo-graph

Structural graph memory for AI coding assistants. Map your codebase. Navigate by structure. Read only what matters.

repo-graph gives LLMs a map of your codebase — entities, relationships, and flows — so they can navigate to the right files without reading everything first.

Instead of flooding an LLM's context window with your entire codebase (or hoping it guesses right), repo-graph builds a lightweight graph of what exists, how things connect, and where the entry points are. The LLM queries the graph, finds the minimal set of files it needs, and reads only those.

The problem

LLMs working on code waste most of their context on orientation:

• Reading files that turn out to be irrelevant
• Missing connections between components in different languages
• Not knowing where a feature starts or what it touches
• Loading 50 files when 5 would do

This is expensive, slow, and gets worse as codebases grow.

How repo-graph solves it

repo-graph scans your codebase once and builds a graph of:

• Entities: modules, packages, classes, functions, routes, services, components
• Relationships: imports, calls, handles, defines, contains
• Flows: end-to-end paths from entry point to data layer

Then it exposes 12 MCP tools that let the LLM:

1. Orient — "What languages are in this repo? What are the main features?"
2. Navigate — "Trace the login flow from route to database" / "What's the shortest path between UserService and the payments API?"
3. Scope — "How many lines would I need to read to understand this feature?" / "Give me just the files I need for this bug fix"
4. Assess — "What's the blast radius of changing this function?" / "Which files are the biggest maintenance risks?"

The LLM gets structural context in a few hundred tokens instead of reading thousands of lines.

Supported languages

Language	Detection	What it extracts
Go	go.mod	Packages, functions, HTTP routes (gin/echo/chi/stdlib), imports
Rust	Cargo.toml	Crates, modules, structs, traits, functions, routes (Actix/Rocket/Axum)
TypeScript	tsconfig.json	Modules, classes, functions, import relationships
React	react in package.json	Components, hooks, context providers, React Router routes, fetch/axios calls, flows
Angular	@angular/core in package.json	Components, services, guards, DI injection, HTTP calls, feature flows
Python	pyproject.toml / setup.py / requirements.txt	Packages, modules, classes, functions, routes (Flask/FastAPI/Django)
Java/Kotlin	pom.xml / build.gradle	Packages, classes, routes (Spring/JAX-RS)
C#/.NET	.csproj / .sln	Namespaces, classes, routes (ASP.NET/Minimal API)
Ruby	Gemfile / .gemspec	Files, classes, modules, routes (Rails)
PHP	composer.json	Namespaces, classes, interfaces, routes (Laravel/Symfony)
Swift	Package.swift / .xcodeproj	Files, types (class/struct/enum/protocol/actor), routes (Vapor)
C/C++	CMakeLists.txt / Makefile / meson.build	Sources, headers, classes, structs, enums, namespaces, includes
SCSS	.scss files present	File-level bloat analysis (selector blocks, sizes)

Multiple analyzers can match one repo (e.g., Go backend + Angular frontend + SCSS). Each contributes its nodes and edges into a single unified graph.

Install

pip install mcp-repo-graph

Requires Python 3.11+. Only runtime dependency: mcp[cli].

Quick start

1. Generate the graph

repo-graph-generate --repo /path/to/your/project

This scans the codebase and writes graph data to .ai/repo-graph/ inside the target repo.

2. Connect to your AI assistant

Add to your MCP configuration:

Claude Code (~/.claude/claude_code_config.json or project .mcp.json):

{
  "mcpServers": {
    "repo-graph": {
      "command": "repo-graph",
      "args": ["--repo", "/path/to/your/project"]
    }
  }
}

With environment variable:

{
  "mcpServers": {
    "repo-graph": {
      "command": "repo-graph",
      "env": { "REPO_GRAPH_REPO": "/path/to/your/project" }
    }
  }
}

3. Use it

The AI assistant now has access to all 12 tools. Example queries it can answer:

• "What does this codebase do?" -> status tool
• "Trace the checkout flow" -> flow tool
• "What would break if I change UserService?" -> impact tool
• "What files do I need for this bug?" -> minimal_read tool
• "This file is too big, how should I split it?" -> split_plan tool
• "Show me the auth flow visually" -> graph_view tool

4. Keep it fresh with a git hook (recommended)

Add repo-graph-generate to a pre-commit hook so the graph stays up to date automatically — no LLM context spent on regeneration:

# .git/hooks/pre-commit (or add to your existing hook)
#!/bin/sh
repo-graph-generate --repo .
git add .ai/repo-graph/

chmod +x .git/hooks/pre-commit

Every commit keeps the graph current. The LLM always has a fresh map without wasting a single token on generate.

Tip: If you don't want graph data in version control, add .ai/repo-graph/ to .gitignore and skip the git add line — the graph will just live locally.

MCP tools reference

Generation

Tool	Parameters	Description
generate	(none)	Scan the codebase from scratch, rebuild the graph, and reload
reload	(none)	Reload graph data from disk (after external repo-graph-generate)

Navigation

Tool	Parameters	Description
status	(none)	Repo overview: git state, detected languages, entity counts, available flows
flow	feature	End-to-end flow for a feature — from entry point through service layer to data
trace	from_id, to_id	Shortest path between any two nodes in the graph
impact	node_id, direction (upstream/downstream), depth	Fan out from a node to see what it affects or depends on
neighbours	node_id	All direct connections to and from a node

Context budgeting

Tool	Parameters	Description
cost	feature	Total line count for all files in a feature's flow
hotspots	top_n	Files ranked by size * connections — maintenance risk indicators
minimal_read	feature, task_hint	Smallest file set needed for a specific task within a feature

Health analysis

Tool	Parameters	Description
bloat_report	file_path	Internal structure of a file: functions/methods ranked by size, type counts
split_plan	file_path	Concrete suggestions for splitting an oversized file, grouped by responsibility
graph_view	feature or node, depth	Visual ASCII map of a feature flow, node neighbourhood, or full graph overview

How it works

1. Detect — scan_project_dirs() finds project roots (including monorepo layouts like packages/*, apps/*, services/*, src/*). Each analyzer checks for its marker files.
2. Scan — matching analyzers extract entities and relationships using regex heuristics. No AST parsing, no external toolchains, no build step required.
3. Merge — all analyzer results merge into a single graph. Nodes deduplicate by ID, edges by (from, to, type).
4. Serve — the MCP server loads the graph into memory and exposes BFS-based traversal tools.

Graph data format

Generated files live in .ai/repo-graph/ inside the target repo:

• nodes.json — [{id, type, name, file_path}, ...]
• edges.json — [{from, to, type}, ...]
• flows/*.yaml — named feature flows with ordered step sequences
• state.md — human-readable snapshot for quick orientation

Edge types: imports, defines, contains, uses, calls, handles, handled_by, exports, includes.

Adding a new analyzer

Create repo_graph/analyzers/<language>.py:

from .base import AnalysisResult, Edge, LanguageAnalyzer, Node, scan_project_dirs, rel_path, read_safe

class MyLangAnalyzer(LanguageAnalyzer):

    @staticmethod
    def detect(repo_root):
        # Check for language marker files
        return any(
            (d / "my-marker").exists()
            for d in scan_project_dirs(repo_root)
        )

    def scan(self):
        nodes, edges = [], []
        # ... scan files, extract entities, build relationships ...
        return AnalysisResult(
            nodes=nodes,
            edges=edges,
            state_sections={"MyLang": f"{len(nodes)} entities\n"},
        )

    # Optional: file-level analysis for bloat_report / split_plan
    def supported_extensions(self):
        return {".mylang"}

    def analyze_file(self, file_path):
        # Return dict with function/method sizes, class counts, etc.
        pass

    def format_bloat_report(self, analysis):
        # Format the analysis dict into a human-readable string
        pass

Register it in analyzers/__init__.py by adding it to _analyzer_classes().

License

MIT

Support

If repo-graph saved you time, consider buying me a coffee.

buymeacoffee.com/polycrisis