code-pathfinder

Code Pathfinder provides an MCP server designed to empower AI coding assistants with deep code intelligence. By building call graphs, data flow graphs, and taint tracking, it allows AI agents to reason about code structure and security patterns beyond simple text search. The MCP server integrates with SecureFlow workflows and enables context-rich interactions for Claude, GPT, and other AI copilots, giving them access to detailed code context, including cross-file data flows and potential vulnerability paths. To get started, run the MCP server and connect your AI agent to the endpoint exposed by the server. Use the provided tooling to evolve security-aware prompts and queries that leverage the server’s structural analysis features, triage results with LLM-assisted prioritization, and surface actionable insights within your development and CI/CD workflows.

Prerequisites:

• A machine with Node.js, Python, or Docker installed depending on your deployment preference. If you’re unsure, follow the official quickstart docs linked in the repository.
• Access to the project repository and its build/installation instructions.

Step-by-step installation (generic, based on common MCP server setups):

1. Clone the repository: git clone https://github.com/shivasurya/code-pathfinder.git cd code-pathfinder

2. Choose an installation method (see official docs for details):

   • Homebrew (recommended on macOS/Linux):
     • Ensure Homebrew is installed: https://brew.sh
     • Follow the project’s quickstart guide to install the MCP server via brew if provided in your release.
   • Docker: pull and run the MCP server image if provided by the maintainer.
   • Native installation (Python/Go/Node as applicable): follow the language-specific setup from the docs.

3. Install dependencies and verify install:

   • For Docker-based: docker pull codepathfinder/mcp-server:latest
   • For native installs: install required language runtimes and package managers (e.g., pipx, npm) as described in the docs.

4. Start the MCP server:

   • If using the provided script: ./scripts/start-mcp.sh
   • If using a direct runtime: follow the runtime-specific start command (e.g., node server.js, python -m mcp_server, or uvx for Python setups).

5. Validate the server is running by hitting the MCP endpoint (as documented in the README’s quickstart page) and ensure you can connect from your AI tooling.

Note: For exact commands, prerequisites, and environment variable requirements, refer to the project’s official quickstart guide: https://codepathfinder.dev/docs/quickstart

Tips and considerations:

• Environment variables: you may need API keys for SecureFlow or LLM providers. Keep keys in environment variables and avoid hard-coding secrets.
• SARIF and CI/CD: The MCP server outputs structured results suitable for CI pipelines and DefectDojo integrations; enable SARIF export if you use GitHub Advanced Security.
• Custom rules: If you plan to write Python-based rules, install the PathFinder SDK and place your rules where the MCP server can load them.
• Debugging: If the MCP server fails to start, check logs for port bindings, dependency mismatches, or missing runtime components. Ensure network access between the MCP server and your AI tooling.
• Compatibility: The server supports AI copilots and internal tooling for code graph analysis; verify that your AI provider is compatible with the SecureFlow-enabled workflows described in the docs.