How do I install the mcp-kling MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "mcp-kling": { "command": "npx", "args": ["-y", "mcp-kling"] } }

What does mcp-kling do?

MCP server for Kling AI video generation

Is the mcp-kling MCP server free to use?

Yes. mcp-kling is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

How do I install the mcp-kling MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "mcp-kling": { "command": "npx", "args": ["-y", "mcp-kling"] } }

What does mcp-kling do?

MCP server for Kling AI video generation

Is the mcp-kling MCP server free to use?

Yes. mcp-kling is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

KlingMCP

A Model Context Protocol (MCP) server for AI video generation using Kling through the AceDataCloud API.

Generate AI videos, extend clips, and transfer motion directly from Claude, VS Code, or any MCP-compatible client.

Features

Text to Video - Create AI-generated videos from text prompts
Image to Video - Generate videos using reference start/end images
Video Extension - Extend existing videos with additional content
Motion Transfer - Transfer motion from a reference video to a character image
Multiple Models - Support for 9 Kling models, including V3, V3 Omni, and canonical Kling O1
Camera Control - Fine-grained camera movement control
Task Tracking - Monitor generation progress and retrieve results

Tool Reference

Tool	Description
`kling_generate_video`	Generate AI video from a text prompt using Kling.
`kling_generate_video_from_image`	Generate AI video using reference images as start and/or end frames.
`kling_extend_video`	Extend an existing video with additional content.
`kling_generate_motion`	Transfer motion from a reference video to a character image.
`kling_get_task`	Query the status and result of a video generation task.
`kling_get_tasks_batch`	Query multiple video generation tasks at once.
`kling_list_models`	List all available Kling models for video generation.
`kling_list_actions`	List all available Kling API actions and corresponding tools.

Quick Start

1. Get Your API Token

Sign up at AceDataCloud Platform
Go to the API documentation page
Click "Acquire" to get your API token
Copy the token for use below

2. Use the Hosted Server (Recommended)

AceDataCloud hosts a managed MCP server — no local installation required.

Endpoint: https://kling.mcp.acedata.cloud/mcp

All requests require a Bearer token. Use the API token from Step 1.

Claude.ai

Connect directly on Claude.ai with OAuth — no API token needed:

Go to Claude.ai Settings → Integrations → Add More
Enter the server URL: https://kling.mcp.acedata.cloud/mcp
Complete the OAuth login flow
Start using the tools in your conversation

Claude Desktop

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (.cursor/mcp.json or .windsurf/mcp.json):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

VS Code (Copilot)

Add to your VS Code MCP config (.vscode/mcp.json):

config.json

{
  "servers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which registers the hosted MCP servers with one-click setup.

JetBrains IDEs

Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
Click Add → HTTP
Paste:

config.json

{
  "mcpServers": {
    "kling": {
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Claude Code

Claude Code supports MCP servers natively:

Terminal

claude mcp add kling --transport http https://kling.mcp.acedata.cloud/mcp \
  -h "Authorization: Bearer YOUR_API_TOKEN"

Or add to your project's .mcp.json:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cline

Add to Cline's MCP settings (.cline/mcp_settings.json):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Amazon Q Developer

Add to your MCP configuration:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Roo Code

Add to Roo Code MCP settings:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Continue.dev

Add to .continue/config.yaml:

yaml

mcpServers:
  - name: kling
    type: streamable-http
    url: https://kling.mcp.acedata.cloud/mcp
    headers:
      Authorization: "Bearer YOUR_API_TOKEN"

Zed

Add to Zed's settings (~/.config/zed/settings.json):

config.json

{
  "language_models": {
    "mcp_servers": {
      "kling": {
        "url": "https://kling.mcp.acedata.cloud/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_TOKEN"
        }
      }
    }
  }
}

cURL Test

bash

# Health check (no auth required)
curl https://kling.mcp.acedata.cloud/health

# MCP initialize
curl -X POST https://kling.mcp.acedata.cloud/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

3. Or Run Locally (Alternative)

If you prefer to run the server on your own machine:

server.ts

# Install from PyPI
pip install mcp-kling
# or
uvx mcp-kling

# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"

# Run (stdio mode for Claude Desktop / local clients)
mcp-kling

# Run (HTTP mode for remote access)
mcp-kling --transport http --port 8000

Claude Desktop (Local)

config.json

{
  "mcpServers": {
    "kling": {
      "command": "uvx",
      "args": ["mcp-kling"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_token_here"
      }
    }
  }
}

Docker (Self-Hosting)

Terminal

docker pull ghcr.io/acedatacloud/mcp-kling:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-kling:latest

Clients connect with their own Bearer token — the server extracts the token from each request's Authorization header.

Available Models

Model	Description	Use Case
`kling-v1`	First generation	Basic video generation
`kling-v1-6`	V1 extended	Improved quality over v1
`kling-v2-master`	V2 master (default)	High-quality, balanced performance
`kling-v2-1-master`	V2.1 master	Enhanced quality and consistency
`kling-v2-5-turbo`	V2.5 turbo	Faster generation, good quality
`kling-o1`	Kling O1	Omni image/video reference generation

Configuration

Environment Variables

Variable	Description	Default
`ACEDATACLOUD_API_TOKEN`	API token from AceDataCloud	Required
`ACEDATACLOUD_API_BASE_URL`	API base URL	`https://api.acedata.cloud`
`KLING_DEFAULT_MODEL`	Default video model	`kling-v2-master`
`KLING_DEFAULT_MODE`	Default generation mode	`std`
`KLING_DEFAULT_ASPECT_RATIO`	Default aspect ratio	`16:9`
`KLING_REQUEST_TIMEOUT`	Request timeout in seconds	`300`
`LOG_LEVEL`	Logging level	`INFO`

Command Line Options

bash

mcp-kling --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

bash

# Clone repository
git clone https://github.com/AceDataCloud/KlingMCP.git
cd KlingMCP

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

bash

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

bash

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

bash

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

Code

KlingMCP/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for Kling API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   ├── oauth.py           # OAuth 2.1 provider
│   ├── server.py          # MCP server initialization
│   ├── types.py           # Type definitions
│   └── utils.py           # Utility functions
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── video_tools.py     # Video generation tools
│   ├── motion_tools.py    # Motion transfer tools
│   ├── task_tools.py      # Task query tools
│   └── info_tools.py      # Information tools
├── prompts/                # MCP prompts
│   └── __init__.py        # Prompt templates
├── tests/                  # Test suite
│   ├── conftest.py
│   └── __init__.py
├── deploy/                 # Deployment configs
│   └── production/
│       ├── deployment.yaml
│       ├── ingress.yaml
│       └── service.yaml
├── .env.example           # Environment template
├── CHANGELOG.md
├── Dockerfile             # Docker image for HTTP mode
├── docker-compose.yaml    # Docker Compose config
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Kling API:

Kling Videos API - Video generation (text2video, image2video, extend)
Kling Motion API - Motion transfer
Kling Tasks API - Task queries

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing)
Open a Pull Request

License

MIT License - see LICENSE for details.

Links

Made with love by AceDataCloud

KlingMCP

A Model Context Protocol (MCP) server for AI video generation using Kling through the AceDataCloud API.

Generate AI videos, extend clips, and transfer motion directly from Claude, VS Code, or any MCP-compatible client.

Features

Text to Video - Create AI-generated videos from text prompts
Image to Video - Generate videos using reference start/end images
Video Extension - Extend existing videos with additional content
Motion Transfer - Transfer motion from a reference video to a character image
Multiple Models - Support for 9 Kling models, including V3, V3 Omni, and canonical Kling O1
Camera Control - Fine-grained camera movement control
Task Tracking - Monitor generation progress and retrieve results

Tool Reference

Tool	Description
`kling_generate_video`	Generate AI video from a text prompt using Kling.
`kling_generate_video_from_image`	Generate AI video using reference images as start and/or end frames.
`kling_extend_video`	Extend an existing video with additional content.
`kling_generate_motion`	Transfer motion from a reference video to a character image.
`kling_get_task`	Query the status and result of a video generation task.
`kling_get_tasks_batch`	Query multiple video generation tasks at once.
`kling_list_models`	List all available Kling models for video generation.
`kling_list_actions`	List all available Kling API actions and corresponding tools.

Quick Start

1. Get Your API Token

Sign up at AceDataCloud Platform
Go to the API documentation page
Click "Acquire" to get your API token
Copy the token for use below

2. Use the Hosted Server (Recommended)

AceDataCloud hosts a managed MCP server — no local installation required.

Endpoint: https://kling.mcp.acedata.cloud/mcp

All requests require a Bearer token. Use the API token from Step 1.

Claude.ai

Connect directly on Claude.ai with OAuth — no API token needed:

Go to Claude.ai Settings → Integrations → Add More
Enter the server URL: https://kling.mcp.acedata.cloud/mcp
Complete the OAuth login flow
Start using the tools in your conversation

Claude Desktop

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (.cursor/mcp.json or .windsurf/mcp.json):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

VS Code (Copilot)

Add to your VS Code MCP config (.vscode/mcp.json):

config.json

{
  "servers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Or install the Ace Data Cloud MCP extension for VS Code, which registers the hosted MCP servers with one-click setup.

JetBrains IDEs

Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
Click Add → HTTP
Paste:

config.json

{
  "mcpServers": {
    "kling": {
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Claude Code

Claude Code supports MCP servers natively:

Terminal

claude mcp add kling --transport http https://kling.mcp.acedata.cloud/mcp \
  -h "Authorization: Bearer YOUR_API_TOKEN"

Or add to your project's .mcp.json:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Cline

Add to Cline's MCP settings (.cline/mcp_settings.json):

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Amazon Q Developer

Add to your MCP configuration:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Roo Code

Add to Roo Code MCP settings:

config.json

{
  "mcpServers": {
    "kling": {
      "type": "streamable-http",
      "url": "https://kling.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_TOKEN"
      }
    }
  }
}

Continue.dev

Add to .continue/config.yaml:

yaml

mcpServers:
  - name: kling
    type: streamable-http
    url: https://kling.mcp.acedata.cloud/mcp
    headers:
      Authorization: "Bearer YOUR_API_TOKEN"

Zed

Add to Zed's settings (~/.config/zed/settings.json):

config.json

{
  "language_models": {
    "mcp_servers": {
      "kling": {
        "url": "https://kling.mcp.acedata.cloud/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_TOKEN"
        }
      }
    }
  }
}

cURL Test

bash

# Health check (no auth required)
curl https://kling.mcp.acedata.cloud/health

# MCP initialize
curl -X POST https://kling.mcp.acedata.cloud/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

3. Or Run Locally (Alternative)

If you prefer to run the server on your own machine:

server.ts

# Install from PyPI
pip install mcp-kling
# or
uvx mcp-kling

# Set your API token
export ACEDATACLOUD_API_TOKEN="your_token_here"

# Run (stdio mode for Claude Desktop / local clients)
mcp-kling

# Run (HTTP mode for remote access)
mcp-kling --transport http --port 8000

Claude Desktop (Local)

config.json

{
  "mcpServers": {
    "kling": {
      "command": "uvx",
      "args": ["mcp-kling"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_token_here"
      }
    }
  }
}

Docker (Self-Hosting)

Terminal

docker pull ghcr.io/acedatacloud/mcp-kling:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-kling:latest

Clients connect with their own Bearer token — the server extracts the token from each request's Authorization header.

Available Models

Model	Description	Use Case
`kling-v1`	First generation	Basic video generation
`kling-v1-6`	V1 extended	Improved quality over v1
`kling-v2-master`	V2 master (default)	High-quality, balanced performance
`kling-v2-1-master`	V2.1 master	Enhanced quality and consistency
`kling-v2-5-turbo`	V2.5 turbo	Faster generation, good quality
`kling-o1`	Kling O1	Omni image/video reference generation

Configuration

Environment Variables

Variable	Description	Default
`ACEDATACLOUD_API_TOKEN`	API token from AceDataCloud	Required
`ACEDATACLOUD_API_BASE_URL`	API base URL	`https://api.acedata.cloud`
`KLING_DEFAULT_MODEL`	Default video model	`kling-v2-master`
`KLING_DEFAULT_MODE`	Default generation mode	`std`
`KLING_DEFAULT_ASPECT_RATIO`	Default aspect ratio	`16:9`
`KLING_REQUEST_TIMEOUT`	Request timeout in seconds	`300`
`LOG_LEVEL`	Logging level	`INFO`

Command Line Options

bash

mcp-kling --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

bash

# Clone repository
git clone https://github.com/AceDataCloud/KlingMCP.git
cd KlingMCP

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

bash

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

bash

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

bash

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

Code

KlingMCP/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for Kling API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   ├── oauth.py           # OAuth 2.1 provider
│   ├── server.py          # MCP server initialization
│   ├── types.py           # Type definitions
│   └── utils.py           # Utility functions
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── video_tools.py     # Video generation tools
│   ├── motion_tools.py    # Motion transfer tools
│   ├── task_tools.py      # Task query tools
│   └── info_tools.py      # Information tools
├── prompts/                # MCP prompts
│   └── __init__.py        # Prompt templates
├── tests/                  # Test suite
│   ├── conftest.py
│   └── __init__.py
├── deploy/                 # Deployment configs
│   └── production/
│       ├── deployment.yaml
│       ├── ingress.yaml
│       └── service.yaml
├── .env.example           # Environment template
├── CHANGELOG.md
├── Dockerfile             # Docker image for HTTP mode
├── docker-compose.yaml    # Docker Compose config
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Kling API:

Kling Videos API - Video generation (text2video, image2video, extend)
Kling Motion API - Motion transfer
Kling Tasks API - Task queries

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing)
Open a Pull Request

License

MIT License - see LICENSE for details.

Links

Made with love by AceDataCloud

Mcp Kling

Quick Install

Install Config Generator

Documentation Overview

KlingMCP

Features

Tool Reference

Quick Start

1. Get Your API Token

2. Use the Hosted Server (Recommended)

Claude.ai

Claude Desktop

Cursor / Windsurf

VS Code (Copilot)

JetBrains IDEs

Claude Code

Cline

Amazon Q Developer

Roo Code

Continue.dev

Zed

cURL Test

3. Or Run Locally (Alternative)

Claude Desktop (Local)

Docker (Self-Hosting)

Available Models

Configuration

Environment Variables

Command Line Options

Development

Setup Development Environment

Run Tests

Code Quality

Build & Publish

Project Structure

API Reference

Contributing

License

Links

Related MCP Servers

Frequently Asked Questions about Mcp Kling

AllMCPs Directory Badge

Technical Specs & Signals

AllMCPs Server

Own this project?

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Mcp Kling

Quick Install

Install Config Generator

Documentation Overview

KlingMCP

Features

Tool Reference

Quick Start

1. Get Your API Token

2. Use the Hosted Server (Recommended)

Claude.ai

Claude Desktop

Cursor / Windsurf

VS Code (Copilot)

JetBrains IDEs

Claude Code

Cline

Amazon Q Developer

Roo Code

Continue.dev

Zed

cURL Test

3. Or Run Locally (Alternative)

Claude Desktop (Local)

Docker (Self-Hosting)

Available Models

Configuration

Environment Variables

Command Line Options

Development

Setup Development Environment

Run Tests

Code Quality