No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by CrowdStrike · MCP Server · ★ 256
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is falcon-mcp safe to install? View the security audit →
falcon-mcp falcon-mcp is a Model Context Protocol (MCP) server that connects AI agents with the CrowdStrike Falcon platform, powering intelligent security analysis in your agentic workflows. It delivers programmatic access to essential security capabilities—including detections, incidents, and behaviors—establishing the foundation for advanced security operations and automation. [!IMPORTANT] 🚧 Public Preview: This project is currently in public preview and under active development. Features and functionality may change before the stable 1.0 release. While we encourage exploration and testing, please avoid production deployments. We welcome your feedback through GitHub Issues to help shape the final release. Table of Contents API Credentials \& Required Scopes [Setting Up CrowdStr
| Stars | 256 |
| Forks | 84 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.6606706392827/100 |
| Open Issues | 27 |
| Last Updated | 2026-09-17 |
| Created | 2025-06-10 |
| Platforms | mcp, python |
| Est. Tokens | ~21k |
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falcon-mcp is Connect AI agents to CrowdStrike Falcon for automated security analysis and threat hunting. It is categorized as a MCP Server with 256 GitHub stars.
falcon-mcp is primarily written in Python. It covers topics such as ai, crowdstrike, falcon.
You can find installation instructions and usage details in the falcon-mcp GitHub repository at github.com/CrowdStrike/falcon-mcp. The project has 256 stars and 84 forks, indicating an active community.
falcon-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to falcon-mcp on Agent Skills Hub include security-skills, LLM-Finetuning-Toolkit, llms_tool. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: