Studio State MCP Server

@dkf2studios

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

dkf2studios

提交者

Luke Johnson

关于 Studio State MCP Server

Stateless MCP server that keeps AI-generated characters, locations, and shots consistent across a film/video project — tells your AI assistant where the production stands, a scene's per-shot status, and marks shots fired/locked. For creators using Higgsfield, Runway, Kling, Seeda

#mcp-ai-film-ai-video-generative-video-character-consistency-continuity-shot-tracking-filmmaking-claude-desktop-higgsfield-runway-kling

Studio State MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "studio-state": {
      "command": "python3",
      "args": [
        "/absolute/path/to/studio-state-public/server.py"
      ],
      "env": {
        "STUDIO_ROOT": "/absolute/path/to/your_show"
      }
    }
  }
}

Studio State MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Studio State?

Studio State is a small, stateless Model Context Protocol (MCP) server for AI film and video production. It reads a project's plain files to answer three questions an AI filmmaking assistant constantly needs to re-establish: orient the session, get shot status, and update a shot. It’s designed for people producing AI-generated film, video, episodic shows, and animation with tools like Higgsfield, Runway, Kling, Seedance, nano-banana, Veo, and ElevenLabs.

How to use Studio State?

Install Python 3.9+ and the MCP SDK (pip install "mcp[cli]"), set the STUDIO_ROOT environment variable to the path of your project folder, then run python3 server.py. Register the server in Claude Desktop via claude_desktop_config.json using the server’s file path and the STUDIO_ROOT environment variable.

Key features of Studio State

Read-dominant: 2 of 3 tools are pure reads.
Stateless: no server-side store or daemon state.
Path-sandboxed: validated scene/episode arguments.
Crash-proof and always current; re-reads files on every call.
Never mutates canonical files; writes only to a sidecar shot_status.json.
Expects a lightweight, file-based project layout.

Use cases of Studio State

Re-establishing production context at the start of a Claude session.
Tracking per-shot status (planned, pending, fired, locked, needs-rework, dropped) across an episode.
Marking a shot as fired or locked and attaching a generation job and still.
Keeping characters, locations, and shots consistent across a long generative video production.

常见问题

What project layout does Studio State require?

It expects a folder with `_pipeline/STATE.json` (project phase, episode, budget, scenes) and episodes organized as `episodes/episode_XXX/scenes/<SCENE>/` containing the shot plan and render manifest files.

Is Studio State safe for my production files?

Yes. It is read-dominant (two of three tools are pure reads). The only writer creates a per-scene `shot_status.json` sidecar — it never modifies your shot plans, render manifests, or state file. Writes are atomic and path-sandboxed.

What dependencies or runtime are required?

Python 3.9+ and the MCP SDK (`mcp[cli]`) are required. The server runs as a Python script and communicates via the Model Context Protocol using stdio.

Can I try Studio State before using my own project?

Yes, a bundled demo project is included. Run `python3 tests/test_acceptance.py` to verify the server works (expects 4/4 checks passed).

What transport or authentication does Studio State use?

The README does not specify OAuth or API-key authentication; it is configured as a local MCP server via Claude Desktop’s stdio transport with an environment variable for the project root.

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