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  1. Home
  2. 🎥 Multimedia Process
  3. Shotflow MCP
Shotflow MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 5:02:46 PM

Shotflow MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
View RepositoryVisit Website

ShotFlow MCP Server — AI分镜脚本与视频生成智能体,支持生成专业分镜、视频、镜头重生成、配额查询等能力。

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "shotflow-mcp": {
      "url": "https://aishotflow.cn/mcp"
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🎥 More in Multimedia Process

Documentation Overview

ShotFlow MCP Server

让 AI Agent 直接调用专业分镜脚本生成能力

MCP Version License Online

ShotFlow MCP Server 是 ShotFlow 的 Model Context Protocol 接口实现,让任何支持 MCP 的 AI 客户端(Claude Desktop、Cursor、Cline、VS Code Copilot 等)都能直接调用专业分镜脚本与视频生成能力。


在线使用

无需安装,直接配置 URL 即可使用:

Code
https://aishotflow.cn/mcp

功能一览

Tool说明
generate_storyboard从剧本/创意描述生成专业分镜脚本(6种创作类型 × 7种视觉风格)
generate_video从分镜镜头生成视频(Pro 功能)
check_video_status查询视频生成状态
regenerate_shot单镜头重新生成
list_video_tasks列出视频任务历史
check_quota查询今日配额
check_health检查服务健康状态
Resource说明
shotflow://templates/creative-types6种创作类型模板与默认参数
shotflow://templates/visual-styles7种视觉风格参考与关键词

快速接入

Claude Desktop

编辑配置文件(macOS: ~/Library/Application Support/Claude/claude_desktop_config.json,Windows: %APPDATA%\Claude\claude_desktop_config.json):

config.json
{
  "mcpServers": {
    "shotflow": {
      "url": "https://aishotflow.cn/mcp"
    }
  }
}

重启 Claude Desktop,点击「Attached tools」确认 shotflow 已加载。

Cursor IDE

在项目根目录创建 .cursor/mcp.json:

config.json
{
  "mcpServers": {
    "shotflow": {
      "url": "https://aishotflow.cn/mcp"
    }
  }
}

VS Code(1.99+)

在项目根目录创建 .vscode/mcp.json:

config.json
{
  "servers": {
    "shotflow": {
      "type": "http",
      "url": "https://aishotflow.cn/mcp"
    }
  }
}

Cline / Roo-Cline

编辑 ~/.cline/mcp_settings.json:

config.json
{
  "mcpServers": {
    "shotflow": {
      "url": "https://aishotflow.cn/mcp"
    }
  }
}

调用示例

生成短视频分镜

告诉你的 AI 客户端:

用 shotflow 的 generate_storyboard 工具,帮我生成一个短视频分镜:一个创业者熬夜做产品的故事,3个场景,温情风格,9:16竖屏

AI 会自动调用 MCP Tool,返回结构化分镜结果。

查看可用创作类型

读取 shotflow 的 creative-types 资源,告诉我有哪些创作类型可选


Agent Card(A2A 协议)

其他 AI Agent 可通过以下 URL 自动发现 ShotFlow 的能力:

Code
https://aishotflow.cn/mcp/agent-card

部署自己的实例

要求

  • Node.js 18+
  • ShotFlow Express Server 运行在 localhost:3003

安装

bash
git clone https://github.com/YOUR_USERNAME/shotflow-mcp.git
cd shotflow-mcp
# 无需 npm install,零依赖

启动

bash
# 开发模式
node shotflow-mcp-server.js
# 默认端口 3004

# 生产部署(systemd)
sudo cp shotflow-mcp.service /etc/systemd/system/
sudo systemctl enable shotflow-mcp
sudo systemctl start shotflow-mcp

环境变量

变量默认值说明
MCP_PORT3004MCP Server 监听端口
SHOTFLOW_API_URLhttp://localhost:3003ShotFlow Express 地址
NODE_ENVdevelopment环境模式

架构

Code
AI Client (Claude/Cursor/...)
    ↓ Streamable HTTP (JSON-RPC 2.0)
Nginx :443
    └── /mcp → ShotFlow MCP Server :3004
                    └── HTTP → ShotFlow Express :3003
  • 协议: MCP 1.0(Streamable HTTP + SSE)
  • 传输: JSON-RPC 2.0 over HTTP
  • 依赖: 零外部依赖(纯 Node.js 实现)
  • SSE 支持: proxy_buffering off 配合 Nginx 实现流式进度推送

文件结构

Code
shotflow-mcp/
├── shotflow-mcp-server.js   # MCP Server 主文件
├── tool-definitions.js       # 7个 Tool 的 JSON Schema 定义
├── resource-definitions.js   # 2个 Resource 定义
├── prompt-builder.js        # Prompt 构造与模型配置解析
├── package.json             # npm 包配置
├── shotflow-mcp.service     # systemd 服务文件
├── INTEGRATION.md          # 详细接入文档
├── README.md               # 本文件
└── test/
    └── test-mcp.js        # 测试套件

测试

bash
# 启动本地服务
node shotflow-mcp-server.js &

# 运行测试
node test/test-mcp.js

相关链接

  • ShotFlow 主站: https://aishotflow.cn
  • MCP 端点: https://aishotflow.cn/mcp
  • Agent Card: https://aishotflow.cn/mcp/agent-card
  • MCP 协议规范: https://modelcontextprotocol.io
  • ShotFlow 公众号: 扫码关注获取最新更新

许可证

MIT License — 自由使用、修改和分发。


支持

  • GitHub Issues: https://github.com/YOUR_USERNAME/shotflow-mcp/issues
  • 邮件: admin@aishotflow.cn

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

Last commit
2mo ago
Most recent push to the default branch.
Directory activity
1 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Shotflow MCP

shotflow-mcp is a hosted MCP server. Add it as a remote server in your client's config: "mcpServers": { "shotflow-mcp": { "url": "https://aishotflow.cn/mcp" } }

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Technical Specs & Signals

Category🎥Multimedia Process
More technical detailsExpand ▾
TransportSSE (Remote)
Last updatedJul 2, 2026
2/4 checks healthy over the last 46d
Views1
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit2mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jul 2, 2026
36Quality signal: Fair · 36/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools15/30
Adoption & activity2/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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