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MCP verified MIT Self-run

Memphora Mcp

mcp-memphora-memphora-mcp · by Memphora

Add persistent memory to AI assistants. Store and recall info across conversations.

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Install

$ agentstack add mcp-memphora-memphora-mcp

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.2 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.2. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

AgentStack Verified badge Links to your public security report.
[![AgentStack Verified](https://agentstack.voostack.com/badges/verified.svg)](https://agentstack.voostack.com/security/report/mcp-memphora-memphora-mcp)

Reliability & compatibility

✓ Security review passed
0 installs to date
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Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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How agent discovery & health will work →
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About

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](LICENSE) for details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.2 Imported from the upstream source.