ShotGrid MCP Server

The ShotGrid MCP Server solves the common pain point of integrating a production‑grade visual effects or animation pipeline with AI assistants that rely on the Model Context Protocol. In traditional setups, developers must write custom wrappers to expose ShotGrid data (assets, tasks, notes) to a language model, often duplicating authentication logic and dealing with rate limits. This server abstracts those complexities into a single, fast‑moving endpoint that speaks the MCP language, allowing Claude or other assistants to query, create, update, and delete ShotGrid entities as if they were native tools.

At its core, the server is a high‑performance implementation built on fastmcp, ensuring low latency and efficient resource use. It provides a full CRUD toolset for ShotGrid objects, so developers can let the assistant create new assets, update task statuses, or delete obsolete entries without leaving the AI workflow. Dedicated thumbnail tools let users download or upload image previews directly, streamlining visual asset management. The note and playlist features extend the assistant’s ability to annotate work items or curate collections, which is invaluable for collaborative production environments.

Key capabilities include:

• Direct ShotGrid API access: Every MCP tool maps to a native ShotGrid endpoint, so authentication and pagination are handled automatically.
• Efficient connection pooling: The server maintains a pool of HTTP connections, reducing overhead when the assistant performs many rapid queries.
• Cross‑platform support: Works on Windows, macOS, and Linux, making it easy to deploy in diverse studio infrastructures.
• Extensive test coverage: The repository ships with a pytest suite and type checks, giving confidence that the MCP surface remains stable as ShotGrid evolves.

Real‑world scenarios where this server shines are plentiful. A production studio can let a creative AI assistant pull the latest task list for a shoot, suggest asset names based on shot descriptions, or automatically generate thumbnail previews when new renders arrive. In a QA pipeline, the assistant can query notes for missing references and create follow‑up tasks without manual API calls. Because the server exposes a standard MCP interface, any existing AI assistant that supports MCP can tap into ShotGrid without custom code, dramatically reducing integration time.

In summary, the ShotGrid MCP Server turns a complex, authenticated API into a clean, low‑latency toolset that plugs directly into AI workflows. Its performance focus, comprehensive CRUD support, and ready‑to‑use thumbnail utilities give developers a powerful bridge between ShotGrid’s production data and the generative capabilities of modern language models.