Skip to main content

MCP Server for Memphora - Add persistent memory to Claude, Cursor, and other AI assistants

Project description

Memphora MCP Server

Add persistent memory to Claude, Cursor, Windsurf, and other AI assistants using the Model Context Protocol (MCP).

What is this?

This MCP server connects your AI assistant to Memphora, giving it the ability to:

  • Remember information across conversations
  • Search your personal knowledge base
  • Extract insights from conversations automatically
  • Recall your preferences, facts, and context

Quick Start

1. Install

# Using pip
pip install memphora-mcp

# Or using uvx (recommended for Claude Desktop)
uvx memphora-mcp

2. Get Your API Key

  1. Go to memphora.ai/dashboard
  2. Create an account or sign in
  3. Copy your API key from the dashboard

3. Configure Claude Desktop

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "memphora": {
      "command": "uvx",
      "args": ["memphora-mcp"],
      "env": {
        "MEMPHORA_API_KEY": "your_api_key_here",
        "MEMPHORA_USER_ID": "your_unique_user_id"
      }
    }
  }
}

4. Restart Claude Desktop

Close and reopen Claude Desktop. You should see the Memphora tools available!

Usage Examples

Storing Memories

Just tell Claude something about yourself:

You: "I work at Google as a software engineer"
Claude: [stores memory] "Got it! I'll remember that you work at Google as a software engineer."

You: "My favorite programming language is Python"
Claude: [stores memory] "Noted! I'll remember that Python is your favorite programming language."

Recalling Memories

Ask Claude about things you've told it before:

You: "Where do I work?"
Claude: [searches memories] "You work at Google as a software engineer."

You: "What programming languages do I like?"
Claude: [searches memories] "Your favorite programming language is Python."

Automatic Context

Claude will automatically search your memories when relevant:

You: "Can you help me with some code?"
Claude: [searches memories for context]
        "Sure! Since you prefer Python and work at Google, I'll write this in Python 
         following Google's style guide..."

Available Tools

Tool Description
memphora_search Search memories for relevant information
memphora_store Store new information for future recall
memphora_extract_conversation Extract memories from a conversation
memphora_list_memories List all stored memories
memphora_delete Delete a specific memory

Configuration Options

Environment Variable Description Default
MEMPHORA_API_KEY Your Memphora API key Required
MEMPHORA_USER_ID Unique identifier for your memories mcp_default_user

Using with Other MCP Clients

Cursor

Add to your Cursor settings:

{
  "mcp": {
    "servers": {
      "memphora": {
        "command": "uvx",
        "args": ["memphora-mcp"],
        "env": {
          "MEMPHORA_API_KEY": "your_api_key_here"
        }
      }
    }
  }
}

Windsurf

Add to your Windsurf MCP configuration:

{
  "mcpServers": {
    "memphora": {
      "command": "python",
      "args": ["-m", "memphora_mcp"],
      "env": {
        "MEMPHORA_API_KEY": "your_api_key_here"
      }
    }
  }
}

Development

Running Locally

# Clone the repo
git clone https://github.com/Memphora/memphora-mcp.git
cd memphora-mcp

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

# Set your API key
export MEMPHORA_API_KEY="your_key"

# Run the server
python -m memphora_mcp

Testing

pytest tests/

Privacy & Security

  • Your memories are stored securely in Memphora's cloud
  • Each user has isolated memory storage
  • API keys are stored locally on your machine
  • All communication is encrypted via HTTPS

Support

License

MIT License - see LICENSE for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

memphora_mcp-0.1.2.tar.gz (6.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

memphora_mcp-0.1.2-py3-none-any.whl (10.5 kB view details)

Uploaded Python 3

File details

Details for the file memphora_mcp-0.1.2.tar.gz.

File metadata

  • Download URL: memphora_mcp-0.1.2.tar.gz
  • Upload date:
  • Size: 6.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for memphora_mcp-0.1.2.tar.gz
Algorithm Hash digest
SHA256 b2bba71d3d2bc12f13af23e8cc333f3b56441a255fd7185dce4b788aedb7a9d6
MD5 56ce1f8d7942c267fc3baecb5a88f58a
BLAKE2b-256 03cd306a7097c5d15cac599edc517d88e32f6b9326e23a3f163a330edb2bb08d

See more details on using hashes here.

File details

Details for the file memphora_mcp-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: memphora_mcp-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 10.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for memphora_mcp-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 ce94b150ed999ba043e397c7d5558116fec04a5e57f007a135a719d520cf20b8
MD5 9ee058167bd3c8dbaa7b889978d84e32
BLAKE2b-256 0158da9734cd373dd6484512c61a3f89474192ee64e3b64e3bb3d63d9b883570

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page