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 DareDev256 · MCP Server · ★ 116
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fcp-mcp-server safe to install? View the security audit →
FCPXML MCP The bridge between Final Cut Pro and AI. 7 grouped tools (62 underlying operations) that turn timeline XML into structured data Claude can read, edit, and generate. Hardened for real libraries: 134 adversarial-input security tests, everywhere, sandboxed writes, no patched binaries, no private APIs — plus a private disclosure channel with externally reported fixes already credited and merged. Real v0.13.0 output: local Whisper transcription, filler-word removal, and phrase-based cutting on a podcast timeline. #
| Stars | 116 |
| Forks | 21 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.8344802408401/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-26 |
| Created | 2026-01-18 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~23k |
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fcp-mcp-server is MCP server for Final Cut Pro XML. Lets Claude read, edit and generate real timelines: cut detection, markers, roles, transcript-based editing. Published on PyPI as fcp-mcp-server.. It is categorized as a MCP Server with 116 GitHub stars.
fcp-mcp-server is primarily written in Python. It covers topics such as ai, anthropic, claude.
You can find installation instructions and usage details in the fcp-mcp-server GitHub repository at github.com/DareDev256/fcp-mcp-server. The project has 116 stars and 21 forks, indicating an active community.
fcp-mcp-server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fcp-mcp-server on Agent Skills Hub include claude-historian-mcp, vibeframe, mcp-server-atlassian-bitbucket. 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: