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

Enables MCP-compatible clients to programmatically control and automate DaVinci Resolve, including reading project/timeline state, building and editing timelines, managing the media pool, applying color operations, and running renders.

A Model Context Protocol implementation that enables LLMs to execute complex, multi-step workflows combining tool usage with cognitive reasoning, providing structured, reusable paths through tasks with advanced control flow.

Enables AI-driven video editing and color grading in DaVinci Resolve, including beat-synced cuts, algorithmic 3D LUT generation, media transcoding, and hands-free playback control through natural language.

Open-source MCP server for controlling Autodesk Fusion 360 from any AI agent, enabling 3D modeling operations via natural language.

A Model Context Protocol server that connects LLMs with Autodesk Fusion, enabling CAD operations through natural language dialogue.

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.

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

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.

An MCP server that turns deployed n8n workflows into callable tools for AI agents, enabling discovery and execution of tagged workflows via a standard interface without rewriting integrations.

Enables an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

Enables MCP clients to run declarative agents and DAG workflows as plain tools, with parallel nodes, review loops, and per-run least-privilege sandboxing.

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

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

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

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

A Model Context Protocol server that converts Markdown content to HTML format.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Enables host-local MCP bridging to Autodesk Fusion for agentic product-development workflows, including raytraced rendering, experimental native drawing automation, and safe Windows setup without admin privileges.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

MCP server for qualifying and responding to inbound leads in seconds using a multi-agent AI pipeline.

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

Enables MCP clients such as Claude Desktop to hand tasks to a running DeepSeek Harness window, where each task becomes a real session visible in the sidebar with a reusable session id. If the in-process plugin is unavailable, it falls back to running the task headless so the tool still returns a result.

Enables AI-powered parametric CAD design in Autodesk Fusion 360 through natural language commands. Supports multiple AI backends (Ollama, OpenAI, Gemini, Claude) with intelligent routing and safety validation for geometric operations.

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.

Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

Enables AI agents to programmatically create, manage, and execute independent Python workflow scripts with full CRUD operations, allowing AI to build and modify automation workflows themselves rather than just executing pre-built ones.