by symgraph · MCP Server · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Featured in: AI Reverse Engineering Tools
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.
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.
| Stars | 52 |
| Forks | 12 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-09 |
| Created | 2026-02-25 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
IDAssistMCP Standalone MCP (Model Context Protocol) server plugin for IDA Pro 9.x that exposes IDA's analysis capabilities to LLM clients like Claude Desktop. Features 38 MCP tools covering binary analysis, decompilation, cross-references, symbol management, type system, navigation, and more 6 consolidated tools with // parameters for comments, variables, types, xrefs, bookmarks, and code 8 MCP...
Looking for a IDAssistMCP alternative? If you're comparing IDAssistMCP with other mcp server tools, these 2 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
A Model Context Protocol (MCP) server that enables AI assistants to interact with IDA Pro for reverse engineer
An AI-powered reverse-engineering copilot for assisting tedious malware analysis in IDA Pro.
Explore other popular mcp server tools:
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: