VideoReceiverMCP

VideoReceiverMCP

MCP server that receives base64-encoded video files, saves them locally, and supports listing and deletion; works via stdio for local agents and HTTP for remote clients.

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README

VideoReceiverMCP

A minimal MCP (Model Context Protocol) server that receives video files over both:

  • stdio — for local agents (Claude Desktop, Cursor)
  • HTTP (Streamable-HTTP) — for remote/network clients

How it works

Sender project                    VideoReceiverMCP server
     |                                      |
     | 1. encode video -> base64 string     |
     | 2. call receive_video(base64) -----> |
     |                                      | 3. decode base64 -> binary
     |                                      | 4. save to received_videos/
     | <-- "Video received! ..."            |

MCP uses JSON-RPC (text only), so videos are base64-encoded before sending.


Setup (local)

# 1. Run the setup script (creates venv + installs deps)
.\setup.ps1

# 2. Activate venv
.\venv\Scripts\Activate.ps1

Run locally

stdio mode (for Claude Desktop / Cursor)

python video_mcp_server.py

HTTP mode (test network transport locally)

python video_mcp_server.py --http
# MCP endpoint: http://localhost:8000/mcp

Configure in Claude Desktop

Edit %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "VideoReceiverMCP": {
      "command": "C:/path/to/mcp/venv/Scripts/python.exe",
      "args": ["C:/path/to/mcp/video_mcp_server.py"]
    }
  }
}

Configure in Cursor / VS Code

Edit .cursor/mcp.json in your project:

{
  "mcpServers": {
    "VideoReceiverMCP": {
      "command": "C:/path/to/mcp/venv/Scripts/python.exe",
      "args": ["C:/path/to/mcp/video_mcp_server.py"]
    }
  }
}

Deploy on Render (free, persistent server)

Why Render and not Netlify? Netlify runs serverless/stateless functions — they cannot keep a persistent connection open, which MCP Streamable-HTTP requires. Render's free tier runs a full persistent Python process.

  1. Push this repo to GitHub
  2. Go to render.com -> New -> Web Service
  3. Connect your GitHub repo — Render auto-reads render.yaml
  4. Deploy! Your MCP endpoint will be:
    https://<your-app>.onrender.com/mcp
    

Connect your other project to the deployed server

# In your other project:
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
import base64, asyncio

async def send_video(video_path: str, server_url: str):
    with open(video_path, "rb") as f:
        b64 = base64.b64encode(f.read()).decode()

    async with streamablehttp_client(server_url) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool(
                "receive_video",
                arguments={"video_base64": b64, "filename": "clip.mp4"}
            )
            print(result.content[0].text)

asyncio.run(send_video("myvideo.mp4", "https://your-app.onrender.com/mcp"))

Available Tools

Tool Args Description
receive_video video_base64, filename?, description? Receives a base64 video, saves to received_videos/
list_received_videos Lists all saved videos
delete_video filename Deletes a video from the server

Test with the included client

# stdio (local)
python send_video_client.py myvideo.mp4

# HTTP (local server running on port 8000)
python send_video_client.py myvideo.mp4 --url http://localhost:8000/mcp

# HTTP (deployed on Render)
python send_video_client.py myvideo.mp4 --url https://your-app.onrender.com/mcp

Limitations

Issue Notes
Size overhead Base64 adds ~33% to file size
Large files Videos >50 MB may strain memory / context windows
Netlify Cannot host persistent MCP HTTP servers — use Render instead

Project structure

mcp/
├── video_mcp_server.py     # MCP server (stdio + HTTP transport)
├── send_video_client.py    # Test client (stdio + HTTP)
├── requirements.txt        # Python dependencies
├── setup.ps1               # One-click setup script
├── render.yaml             # Render deployment config
├── Procfile                # Railway / Heroku deployment
├── .gitignore
└── received_videos/        # Videos saved here (auto-created)

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