"How to automate workflows in DaVinci Resolve and Fusion" matching MCP servers:

Enables natural language control of DaVinci Resolve, allowing Claude to browse projects, edit timelines, apply color grades, render exports, and perform AI-powered frame analysis via Moondream.

Multi-model fusion analysis tool that sends queries to multiple AI models in parallel and returns structured analysis from a judge model.

Enables AI-driven DaVinci Resolve 21 automation with 88 tools for project management, timeline editing, color grading, AI analysis, rendering, and dailies creation.

Connects DaVinci Resolve Studio to Claude AI via the Model Context Protocol, enabling natural language control over video editing, color grading, Fusion compositing, AI features, and more.

Enables MCP clients to call OpenRouter Fusion, which queries a panel of models in parallel and synthesizes a single answer using a judge model, useful for research and critical decisions.

Enables LLMs to reconstruct parametric Fusion 360 models from mesh (STL/3MF) or photo inputs by analyzing geometry and creating editable features via a Fusion 360 add-in bridge.

Enables running and grading AI agent evaluations, and applying runtime guardrails that check or block unsafe tool calls through dedicated guardrail tools.

Replicates OpenRouter Fusion on local Claude CLI. Fans out multiple claude -p calls with diverse analytical lenses, then judges and synthesizes the results for improved answers.

Enables MCP clients to run multi-model deliberation (Fusion) via background jobs, using a panel of models and a judge to synthesize high-quality answers.

Exposes a multi-model council (panel-judge-synthesizer) and a build/check loop as MCP tools, enabling Claude Code or any MCP client to perform structured multi-model analysis and synthesis locally.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

AI-powered MCP server for file renaming, organization, and PDF splitting using natural language prompts and content analysis.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

A stateless remote MCP server that lets AI agents anonymously submit encrypted reports of alignment and safety failures to leaks.md (what others did) and confession.md (what the agent itself did), returning a receipt without accounts or keys. It exposes public tools such as submit_leak and submit_confession over Streamable HTTP.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.