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

Run YAML workflows as MCP tools so agents can automate real tasks with one server.

Run dynamic, multi-agent workflow scripts — agent(), parallel(), pipeline() — over real coding agents (Claude Code and OpenAI Codex), with deterministic journaling, resume, token budgets, and git-worktree isolation.

Enables durable multi-agent workflow runs for Codex with a live terminal dashboard, restartable agents, and bug-finding templates, all controllable through natural language.

Enables AI agents to discover, understand, and execute complex multi-step workflows defined in YAML files through the Model Context Protocol.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

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.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Run provable, .slang-driven multi-agent workflows inside Claude Code. A deterministic executor enforces typed output contracts, tool scoping, and convergence, providing guarantees and auditability.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

Converts any REST API into MCP-compatible tools instantly by providing an OpenAPI/Swagger spec, enabling seamless integration with AI agents.

Enables local opencode agents to control a live Chrome browser via MCP tools, including tab management, JavaScript execution, clicking, form filling, page reading, screenshots, and console log retrieval.

Acts as a bridge between standard REST/OpenAPI web APIs and the Model Context Protocol, dynamically converting REST endpoints into MCP tools for LLM clients like Cursor, Claude Desktop, and n8n.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Connects to Stack AI workflows, allowing you to run workflows and pass user inputs through MCP-compatible clients.

A TypeScript implementation of a Model Context Protocol (MCP) server that exposes Dify workflows as tools for AI systems to interact with.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.