mcp_vms

MCP-compliant server for seamless VMS (CCTV) integration and video access.

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mcp_vms implements an MCP server that bridges CCTV Video Management Systems (VMS) with model context protocols. It retrieves live and recorded video streams, exposes channel information and status, and supports remote video playback control and PTZ camera management. Comprehensive error handling and logging ensure reliable integration with AI tooling requiring contextual video feeds.

Key Features

Retrieve live and recorded video streams from VMS

Fetch channel connection and recording status

Access recording dates and times per channel

Extract live or recorded images from specific channels

Display live video or playback dialogs at defined channels/timestamps

Control PTZ cameras by setting preset positions

Comprehensive error handling and logging

Configuration support for VMS connection

Compatible with Claude desktop via standardized config

Python 3.12+ support with easy setup

Use Cases

Automating AI-driven surveillance analysis workflows

Providing contextual video feeds for model inference pipelines

Enabling remote monitoring and playback of CCTV footage

Integrating real-time camera streams into security dashboards

Supporting incident review with targeted playback controls

Facilitating PTZ camera automation in security systems

Multi-channel video data extraction for training AI models

Connecting VMS infrastructure with context-aware applications

Centralized management of VMS video channels via APIs

Enhancing human-in-the-loop security operations with context tools

README

MCP Server - VMS Integration

A Model Context Protocol (MCP) server designed to connect to a CCTV recording program (VMS) to retrieve recorded and live video streams. It also provides tools to control the VMS software, such as showing live or playback dialogs for specific channels at specified times.

Features

Retrieve video channel information, including connection and recording status.
Fetch recording dates and times for specific channels.
Fetch live or recorded images from video channels.
Show live video streams or playback dialogs for specific channels and timestamps.
Control PTZ (Pan-Tilt-Zoom) cameras by moving them to preset positions.
Comprehensive error handling and logging.

Prerequisites

Python 3.12+
vmspy library (for VMS integration)
Pillow library (for image processing)

MCP-server Configuration

If you want to use mcp-vms with Claude desktop, you need to set up the claude_desktop_config.json file as follows:

json

{
  "mcpServers": {
	"vms": {
	  "command": "uv",
	  "args": [
		"--directory",
		"X:\\path\\to\\mcp-vms",
		"run",
		"mcp_vms.py"
	  ]
	}
  }
}

VMS Connection Configuration

The server uses the following default configuration for connecting to the VMS:

mcp_vms_config.py

python

vms_config = {
    'img_width': 320,
    'img_height': 240,
    'pixel_format': 'RGB',
    'url': '127.0.0.1',
    'port': 3300,
    'access_id': 'admin',
    'access_pw': 'admin',
}

Installation

1. Install UV Package Manager

Run the following command in PowerShell to install UV:

shell

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

For alternative installation methods, see the official UV documentation.

2.Install VMS Server

Download and install the VMS server from:
http://surveillance-logic.com/en/download.html (Required before using this MCP server)

3.Install Python Dependencies

Download the vmspy library:
vmspy1.4-python3.12-x64.zip Extract the contents into your mcp_vms directory

The mcp-vms directory should look like this:

shell

mcp-vms/
├── .gitignore
├── .python-version
├── LICENSE
├── README.md
├── pyproject.toml
├── uv.lock
├── mcp_vms.py            # Main server implementation
├── mcp_vms_config.py     # VMS connection configuration
├── vmspy.pyd             # VMS Python library
├── avcodec-61.dll        # FFmpeg libraries
├── avutil-59.dll
├── swresample-5.dll
├── swscale-8.dll

Star History

Repository Owner

jyjune

User

Repository Details

Language Python

Default Branch main

Size 190 KB

Contributors 3

License MIT License

MCP Verified Nov 12, 2025

Programming Languages

Python

100%

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