A Streamable HTTP MCP Server for Memvid
- • Basic MCP protocol features implemented (12/40)
- • Room for improvement in GitHub community
- • Moderate dependency usage (10/20)
- • Room for improvement in deployment maturity
- • Documentation (8/8)
- • Archestra MCP Trust score badge is missing
{
"memvid-mcp-server": {
"command": "python",
"args": [
"server.py"
],
"env": {}
},
"memvid-mcp-server-configured": {
"command": "python",
"args": [
"server.py"
],
"env": {
"PORT": "3002"
}
}
}memvid-mcp-server
A Streamable-HTTP MCP Server that uses memvid to encode text data into videos that can be quickly looked up with semantic search.
Supported Actions:
add_chunks: Adds chunks to the memory video. Note: each time you add chunks, it resets the memory.mp4. Unsure if there is a way to incrementally add.search: queries for the top-matching chunks. Returns 5 by default, but can be changed with top_k param.
Running
Set up your environment:
python3.11 -m venv my_env
. ./my_env/bin/activate
pip install -r requirements.txt
Run the server:
python server.py
With a custom port:
PORT=3002 python server.py
Connect a Client
You can connect a client to your MCP Server once it's running. Configure per the client's configuration. There is the mcp-config.json that has an example configuration that looks like this:
{
"mcpServers": {
"memvid": {
"type": "streamable-http",
"url": "http://localhost:3000"
}
}
}
Acknowledgements
- Obviously the modelcontextprotocol and Anthropic teams for the MCP Specification. https://modelcontextprotocol.io/introduction
- HeyFerrante for enabling and sponsoring this project.
[](https://archestra.ai/mcp-catalog/ferrants__memvid-mcp-server)memvid-mcp-server
A Streamable-HTTP MCP Server that uses memvid to encode text data into videos that can be quickly looked up with semantic search.
Supported Actions:
add_chunks: Adds chunks to the memory video. Note: each time you add chunks, it resets the memory.mp4. Unsure if there is a way to incrementally add.search: queries for the top-matching chunks. Returns 5 by default, but can be changed with top_k param.
Running
Set up your environment:
python3.11 -m venv my_env
. ./my_env/bin/activate
pip install -r requirements.txt
Run the server:
python server.py
With a custom port:
PORT=3002 python server.py
Connect a Client
You can connect a client to your MCP Server once it's running. Configure per the client's configuration. There is the mcp-config.json that has an example configuration that looks like this:
{
"mcpServers": {
"memvid": {
"type": "streamable-http",
"url": "http://localhost:3000"
}
}
}
Acknowledgements
- Obviously the modelcontextprotocol and Anthropic teams for the MCP Specification. https://modelcontextprotocol.io/introduction
- HeyFerrante for enabling and sponsoring this project.