Code intelligence MCP server: call graphs, type inference, and symbol search for Python/Go.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
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Install:
Scan a Python project (rules download automatically):
Scan Dockerfiles:
No config files, no API keys, no cloud accounts. Results in your terminal in seconds.
Code Pathfinder is an open-source static analysis engine that builds a graph of your codebase and traces how data flows through it. It parses source code into Abstract Syntax Trees, constructs call graphs across files, and runs taint analysis to find source-to-sink vulnerabilities that span multiple files and function boundaries.
v2.0 introduces cross-file dataflow analysis: trace user input from an HTTP handler in one file through helper functions and into a SQL query in another file. This is the kind of analysis that pattern-matching tools miss entirely.
Most open-source SAST tools operate on single files. Code Pathfinder v2.0 tracks tainted data across file boundaries:
The engine builds a Variable Dependency Graph (VDG) per function, then connects them through inter-procedural taint transfer summaries. When user_input flows into a function parameter in another file, the taint propagates through the call graph to the sink.
Rules download from CDN automatically. No need to clone the repo or manage rule files.
| Language | Bundles | Rules | Coverage |
|---|---|---|---|
| Python | django, flask, aws_lambda, cryptography, jwt, lang, deserialization, pyramid | 158 | SQL injection, RCE, SSRF, path traversal, XSS, deserialization, crypto misuse, JWT vulnerabilities |
| Docker | security, best-practice, performance | 37 | Root user, exposed secrets, image pinning, multi-stage builds, layer optimization |
| Docker Compose | security, networking | 10 | Privileged mode, socket exposure, capability escalation, network isolation |
Browse all rules with examples and test cases at the Rule Registry.
Code Pathfinder runs as an MCP server, giving Claude Code, Cursor, Cline, and other AI assistants access to call graphs, data flows, and security analysis. More context than LSP, focused on security and code structure.
The MCP server exposes tools for querying the code graph: find callers/callees, trace data flows, search for patterns, and run security rules β all available to the AI assistant during code review or development.
Security rules are Python scripts using the PathFinder SDK. Define sources, sinks, and sanitizers β the dataflow engine handles the analysis.
Here's a real rule from the repo (PYTHON-DJANGO-SEC-001) that detects SQL injection in Django:
Explore all 190 rules in the rules/ directory or browse the Rule Registry. See the rule writing guide and dataflow documentation to write your own.
See the rule writing guide and dataflow documentation for more.
Installs the CLI binary and Python SDK for writing rules.
Download from GitHub Releases for Linux (amd64, arm64), macOS (Intel, Apple Silicon), and Windows (x64).
See the full example: .github/workflows/code-pathfinder-scan.yml
| Input | Description | Default |
|---|---|---|
rules | Path to local Python rule files or directory | - |
ruleset | Remote ruleset(s), comma-separated (e.g., python/all, docker/security) | - |
project | Path to source code | . |
output | Output format: sarif, json, or csv | sarif |
output-file | Output file path | pathfinder-results.sarif |
fail-on | Fail on severities (e.g., critical,high) | - |
verbose | Enable verbose output | false |
debug | Enable debug diagnostics with timestamps | false |
skip-tests | Skip test files | true |
refresh-rules | Force refresh cached rulesets | false |
disable-metrics | Disable anonymous usage metrics | false |
python-version | Python version to use | 3.12 |
pr-comment | Post summary comment on pull request | false |
pr-inline | Post inline review comments for critical/high findings | false |
github-token | GitHub token (required when pr-comment or pr-inline is enabled) | - |
no-diff | Disable diff-aware scanning (scan all files) | false |
Either rules or ruleset is required.
| Language | Analysis | Status |
|---|---|---|
| Python | Cross-file dataflow, taint analysis, call graphs | Stable |
| Dockerfile | Instruction analysis, security patterns | Stable |
| Docker Compose | Configuration analysis, security patterns | Stable |
| Go | AST analysis, call graphs | Coming soon |
Contributions are welcome. Read the Contributing Guide for setup instructions, how to run tests locally, and the PR process.
In-product announcements (workshops, blog posts, security advisories) are
managed via release/latest.json. Add an entry to announcements[],
open a PR, and once it merges to main the publish workflow uploads the
manifest to the CDN within ~60 seconds. See the version-update-check tech
spec for the schema and version_range semantics.
All contributors must sign the Contributor License Agreement (CLA) before any pull request can be merged.
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