pyghidra-mcp: Python Command-Line Ghidra MCP

by clearbluejar · MCP Server · ★ 443

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install pyghidra-mcp

docker run --rm -p 8000:8000 ghcr.io/clearbluejar/pyghidra-mcp
uvx pyghidra-mcp-cli

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars443
Forks64
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score64/100
Open Issues27
Last Updated2026-10-10
Created2025-07-04
Platformsmcp, python
Est. Tokens~22k

About pyghidra-mcp

PyGhidra-MCP - Ghidra Model Context Protocol Server Overview is a command-line Model Context Protocol (MCP) server that brings the full analytical power of Ghidra, a robust software reverse engineering (SRE) suite, into the world of intelligent agents and LLM-based tooling.

Compatible Skills

These tools work well together with pyghidra-mcp for enhanced workflows:

  • decyx — semantic(0.41)+complementary+same_lang+similar_pop+shared_platform (64%)
  • repren — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (63%)
  • wiki — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (62%)
  • idac — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (62%)
  • sql2json — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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