Store MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Store MCP Serverstore my API key as 'github_token' with value 'ghp_abc123xyz'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Store MCP Server
A Model Context Protocol (MCP) server that enables AI agents to store and retrieve information persistently.
Project Structure
store_mcp/
├── README.md # This file
├── pyproject.toml # Project dependencies and configuration
├── src/
│ └── store_mcp/
│ ├── __init__.py # Package initialization
│ ├── server.py # Main MCP server implementation
│ └── storage.py # Storage backend (JSON/SQLite)
└── tests/
├── __init__.py
└── test_server.py # Unit testsRelated MCP server: Agent Memory MCP Server
Features
Store Information: Save key-value pairs or structured data
Retrieve Information: Query stored data by key or search criteria
List Keys: View all available stored keys
Delete Information: Remove stored data when no longer needed
Persistent Storage: Data persists across sessions
Installation
# Install dependencies
pip install -e .Usage
# Run the MCP server
python -m store_mcp.serverMCP Tools
The server exposes the following tools to AI agents:
store_data: Store information with a keyretrieve_data: Retrieve information by keylist_keys: List all stored keysdelete_data: Delete stored information by keysearch_data: Search stored information by pattern or content
Configuration
MCP Client Configuration
To use this server with an MCP client (like Claude Desktop), add it to your MCP settings configuration file:
For Claude Desktop on MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
For Claude Desktop on Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"store": {
"command": "python",
"args": [
"-m",
"store_mcp.server"
],
"env": {
"PYTHONPATH": "/absolute/path/to/store_mcp/src"
}
}
}
}Alternative using uvx (if installed via pip):
{
"mcpServers": {
"store": {
"command": "uvx",
"args": [
"--from",
"/absolute/path/to/store_mcp",
"python",
"-m",
"store_mcp.server"
]
}
}
}Storage Configuration
The server uses a local file-based storage system (JSON) located at:
Default:
~/.store_mcp/data.json
To use a custom storage location, modify server.py and initialize Storage with a custom path:
storage = Storage("/path/to/custom/data.json")Environment Variables
You can set the following environment variables:
STORE_MCP_PATH: Custom path for the storage file (default:~/.store_mcp/data.json)
Example configuration with custom storage path:
{
"mcpServers": {
"store": {
"command": "python",
"args": ["-m", "store_mcp.server"],
"env": {
"PYTHONPATH": "/absolute/path/to/store_mcp/src",
"STORE_MCP_PATH": "/custom/path/to/storage.json"
}
}
}
}Development
# Run tests
pytest tests/Requirements
Python 3.10+
mcp library
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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Maintenance
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