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MCP server for Joern code property graphs
codebadger sits between an agent and a codebase, building a Joern CPG and exposing it over MCP. The agent can then run CPGQL queries and use tools for data-flow tracing, taint analysis, program slicing, and vulnerability hunting.
Builders who want their agent to analyze repositories through Joern and MCP.
You can ask an agent to trace code structure, data flow, and taint paths without manually grepping the repo.
What it does
Joern-backed code property graphs
Builds a CPG from a git repository, local path, or pasted snippet and serves it to MCP clients.
Queryable analysis tools
Exposes MCP tools for CPGQL queries, code browsing, data-flow tracing, taint analysis, and program slicing.
Multi-language coverage
Works across Java, C/C++, JavaScript, Python, Go, Kotlin, C#, Ghidra, Jimple, PHP, Ruby, and Swift.
Scale-oriented execution
Uses per-CPG worker pools, memory-aware scheduling, and Postgres/Redis backends for larger batches.
Custom detector support
Lets you add your own analysis tools and detectors through the custom tools path.
README
𦑠codebadger
codebadger is a containerized Model Context Protocol (MCP) server that gives AI agents and LLMs deep, queryable access to a codebase's structure and data flow through Joern Code Property Graphs (CPGs).
Point it at a Git repository, a local path, or even a pasted code snippet, and codebadger builds a CPG and exposes it over MCP β so an assistant can run CPGQL queries, trace data flow and taint, slice programs, and hunt for vulnerabilities across Java, C/C++, JavaScript, Python, Go, Kotlin, C#, Ghidra, Jimple, PHP, Ruby, and Swift.
It's a general-purpose foundation for both program analysis (understanding code structure, call graphs, and data flow) and vulnerability analysis (taint tracking, bug hunting, and PoC development) β useful for academic research as well as industry security and engineering work. It's built to scale to large analysis batches with per-CPG worker pools, memory-aware scheduling, and a Postgres/Redis backend.
News
codebadger and its paper - Bridging Code Property Graphs and Language Models for Program Analysis - were accepted at the Software Vulnerability Management Workshop @ ICSE 2026. π
Documentation
Everything a developer or security researcher needs lives in docs/:
| Doc | What's in it |
|---|---|
| Installation | Prerequisites and a 5-minute local setup. |
| Usage | Connecting MCP clients, the tool catalog, and a researcher workflow. |
| LLM workflow guide | Recommended bounded tool sequence for agents. |
| Available Tools | Every MCP tool by category, with a description of what each does. |
| Configuration | config.yaml / env reference, telemetry. |
| Deployment | Postgres/Redis, memory sizing, shared vs pool, large batches. |
| Architecture | System design and diagrams. |
| Security | Threat model, trust boundaries, and production hardening. |
| Custom Tools | Add your own detectors. |
| Contributing | Dev setup, tests, and guidelines. |
| Roadmap | What's shipped and what's next. |
Found a vulnerability using codebadger?
We'd love to hear about it - open a PR adding it to TROPHIES.md (CVE ID, project, one-line description, date).
Citation
@inproceedings{lekssays2026bridging,
title={Bridging Code Property Graphs and Language Models for Program Analysis},
author={Lekssays, Ahmed},
booktitle={Proceedings of the 2026 IEEE/ACM 4th International Workshop on Software Vulnerability Management},
pages={33--40},
year={2026}
}
Files in the repo
- .github
- .ssh-keys
- docs
- examples
- playground
- scripts
- src
- tests
- .dockerignore
- .env.example
- .flake8
- .gitignore
- AGENTS.md
- cleanup.sh
- config.example.yaml
- docker-compose.yml
- Dockerfile
- Dockerfile.mcp
- LICENSE
- main.py
- pyproject.toml
- pytest.ini
- README.md
- requirements.txt
- TROPHIES.md
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