Joern MCP Server

Overview

The Joern MCP Server is a specialized Model Context Protocol (MCP) endpoint that exposes the analytical capabilities of Joern—a powerful static‑analysis framework for software binaries and source code—to AI assistants. By turning Joern’s graph‑based queries, vulnerability detection routines, and code‑navigation tools into MCP resources, developers can ask a language model to perform deep code reviews, locate security flaws, and retrieve contextual information about functions or modules without leaving their conversational workflow.

Problem Solved

Modern software teams often struggle to bridge the gap between static analysis tools and conversational AI. Traditional tooling requires manual queries, scripting, or bespoke integrations, which can be error‑prone and time‑consuming. The Joern MCP Server eliminates this friction by providing a declarative, AI‑friendly interface that lets assistants query the code graph, invoke custom analysis scripts, and return structured results—all through natural language prompts. This streamlines code review cycles, accelerates security audits, and reduces the learning curve for developers who prefer to interact with codebases via chat.

Core Value Proposition

• Seamless AI Integration: The server adheres to MCP standards, allowing any compliant AI assistant (e.g., Claude, GPT‑4o) to discover and invoke its tools without custom adapters.
• Rich Static Analysis: Leveraging Joern’s extensive graph database, the server can answer questions about control flow, data dependencies, and potential vulnerabilities with high precision.
• Custom Tool Extension: Developers can add bespoke analysis routines by writing Scala scripts in or , which the MCP server automatically exposes as callable tools.
• Secure, Authenticated Access: The server supports username/password authentication and can be run behind a firewall or within an internal network, ensuring that sensitive codebases remain protected while still being AI‑accessible.

Key Features

• Graph Query Interface: Exposes Joern’s Cypher‑like query language via MCP, enabling assistants to retrieve nodes, edges, and attributes from the code graph.
• Pre‑defined Security Checks: Built‑in tools that scan for common patterns such as buffer overflows, injection points, and insecure cryptographic usage.
• Source Code Retrieval: The module allows the assistant to fetch raw source snippets for any identified issue, providing context directly within the chat.
• Scalable Resource Management: Configurable memory limits () and port settings let teams tune the server for large codebases or shared environments.
• Extensibility: Adding a new analysis tool is as simple as updating the Scala files and reloading the server; no code changes are required on the AI side.

Use Cases

• Automated Code Review: A developer asks the assistant, “Does this module contain any hard‑coded credentials?” The server runs a pattern search and returns the relevant lines.
• Security Audits: Security teams can request a full scan of a repository for known CWE patterns, receiving a structured report that the assistant can summarize.
• Developer Onboarding: New team members can query “What does do?” and receive a concise explanation along with the underlying code fragment.
• Continuous Integration: Integrate the MCP server into CI pipelines, allowing automated pull‑request comments generated by an AI assistant based on static analysis results.

Integration Flow

1. Discovery: The AI client queries the MCP server’s endpoint to list available tools and resources.
2. Invocation: The assistant constructs a with the desired tool name and parameters (e.g., a Cypher query).
3. Execution: Joern executes the query or script, returning structured JSON to the MCP server.
4. Response: The AI client receives the result, formats it for the user, and may ask follow‑up questions to drill deeper.

Unique Advantages

• Unified Graph Model: Unlike traditional linters that output flat lists, Joern’s graph representation provides context about call chains, data flow, and module dependencies—critical for accurate security assessments.
• Language‑agnostic Analysis: Joern supports multiple programming languages; the MCP server exposes a single interface, letting AI assistants work across heterogeneous codebases.
• Open‑Source Flexibility: Built on top of the free Joern engine, teams can customize analysis logic without licensing constraints, ensuring that AI‑dr