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 samuelgursky · MCP Server · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is davinci-resolve-mcp safe to install? View the security audit →
DaVinci Resolve MCP Server -blue.svg) A Model Context Protocol (MCP) server providing complete coverage of the DaVinci Resolve Scripting API. Connect AI assistants (Claude, Cursor, Windsurf) to DaVinci Resolve and control every aspect of your post-production workflow through natural language. What's New in v2.0.1 26-tool compound server — all 324 API methods grouped into 26 context-efficient tools (default) Universal installer — single for macOS/Windows/Linux, 9 MCP clients Dedicated timelineitem actions — retime/speed, transform, crop, composite, audio, keyframes with validation Lazy Resolve connection — server starts instantly, connects when first tool is called Bug fixes
| Stars | 3,079 |
| Forks | 358 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.8836918799108/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-22 |
| Created | 2025-03-18 |
| Platforms | mcp, python |
| Est. Tokens | ~22k |
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davinci-resolve-mcp is MCP server integration for DaVinci Resolve Studio. It is categorized as a MCP Server with 3.1k GitHub stars.
davinci-resolve-mcp is primarily written in Python. It covers topics such as blackmagic, blackmagic-design, blackmagicdesign.
You can find installation instructions and usage details in the davinci-resolve-mcp GitHub repository at github.com/samuelgursky/davinci-resolve-mcp. The project has 3.1k stars and 358 forks, indicating an active community.
davinci-resolve-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to davinci-resolve-mcp on Agent Skills Hub include OpenMetadata, Skill_Seekers, ida-pro-mcp. 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: