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MCP Server for Mem.ai

MCP Server for Mem.ai

A production-ready Model Context Protocol (MCP) server that provides AI assistants with intelligent access to Mem.ai's knowledge management platform.

Python 3.10+ License: MIT

โœจ Features

  • ๐Ÿง  Intelligent Memory: Save and process content with Mem It's AI-powered organization

  • ๐Ÿ“ Note Management: Create, read, and delete structured markdown notes

  • ๐Ÿ“ Collections: Organize notes into searchable collections

  • ๐Ÿ”’ Type-Safe: Full type hints and Pydantic validation

  • โšก Async/Await: High-performance async I/O throughout

  • ๐ŸŽฏ Clean API: Simple, intuitive interface for AI assistants

  • ๐Ÿ›ก๏ธ Production-Ready: Comprehensive error handling and logging

  • ๐Ÿงช Well-Tested: Full test suite with pytest

๐Ÿ“‹ Prerequisites

๐Ÿš€ Quick Start

Installation

  1. Clone the repository:

git clone https://github.com/yourusername/mcp-mem.ai.git cd mcp-mem.ai
  1. Install dependencies:

pip install -e .
  1. Set up your environment:

cp .env.example .env # Edit .env and add your MEM_API_KEY

Running the Server

Local Development

fastmcp run src/mcp_mem/server.py

Using with Claude Desktop

Add to your Claude Desktop configuration (claude_desktop_config.json):

{ "mcpServers": { "mem": { "command": "python", "args": ["-m", "mcp_mem.server"], "env": { "MEM_API_KEY": "your_api_key_here" } } } }

Using with Other MCP Clients

from mcp_mem import mcp # Run the server mcp.run()

๐Ÿ› ๏ธ Available Tools

1. mem_it - Intelligent Content Processing

Save and automatically process any content type with AI-powered organization.

Parameters:

  • input (required): Content to save (text, HTML, markdown, etc.)

  • instructions (optional): Processing instructions

  • context (optional): Additional context for organization

  • timestamp (optional): ISO 8601 timestamp

Example:

mem_it( input="Just had a great meeting with the product team about Q1 roadmap...", instructions="Extract key action items and decisions", context="Product Planning" )

2. create_note - Create Structured Note

Create a markdown-formatted note with explicit control over content and organization.

Parameters:

  • content (required): Markdown-formatted content

  • collection_ids (optional): List of collection UUIDs

  • collection_titles (optional): List of collection titles

Example:

create_note( content="""# Team Standup - Jan 15, 2024 ## Completed - Feature X shipped to production - Bug fixes for issue #123 ## In Progress - Working on Feature Y - Code review for PR #456 ## Blockers - Waiting for API access """, collection_titles=["Team Meetings", "Engineering"] )

3. read_note - Read Note

Retrieve a note's full content and metadata by ID.

Parameters:

  • note_id (required): UUID of the note

Example:

read_note("01961d40-7a67-7049-a8a6-d5638cbaaeb9")

4. delete_note - Delete Note

Permanently delete a note by ID.

Parameters:

  • note_id (required): UUID of the note

Example:

delete_note("01961d40-7a67-7049-a8a6-d5638cbaaeb9")

5. create_collection - Create Collection

Create a new collection to organize related notes.

Parameters:

  • title (required): Collection title

  • description (optional): Markdown-formatted description

Example:

create_collection( title="Project Apollo", description="""# Project Apollo All notes related to the Apollo project including: - Meeting notes - Technical specifications - Customer feedback """ )

6. delete_collection - Delete Collection

Delete a collection (notes remain, just unassociated).

Parameters:

  • collection_id (required): UUID of the collection

Example:

delete_collection("5e29c8a2-c73b-476b-9311-e2579712d4b1")

โš™๏ธ Configuration

Configuration is done via environment variables. Copy .env.example to .env and customize:

# Required: Your Mem.ai API key MEM_API_KEY=your_api_key_here # Optional: Custom API endpoint (default: https://api.mem.ai/v2) MEM_API_BASE_URL=https://api.mem.ai/v2 # Optional: Request timeout in seconds (default: 30) MEM_REQUEST_TIMEOUT=30 # Optional: Enable debug logging (default: false) MEM_DEBUG=false

๐Ÿ—๏ธ Architecture

src/mcp_mem/ โ”œโ”€โ”€ __init__.py # Package initialization โ”œโ”€โ”€ models.py # Pydantic data models โ”œโ”€โ”€ client.py # Mem.ai API client โ””โ”€โ”€ server.py # MCP server implementation

Key Components

  • models.py: Pydantic models for request/response validation

  • client.py: Async HTTP client wrapper for Mem.ai API

  • server.py: FastMCP server with tool implementations

๐Ÿงช Testing

Run the test suite:

# Install dev dependencies pip install -e ".[dev]" # Run all tests pytest # Run with coverage pytest --cov=mcp_mem --cov-report=html # Run specific test file pytest tests/test_client.py

๐Ÿ” Error Handling

The server provides clear, actionable error messages:

  • MemAuthenticationError: Invalid or missing API key

  • MemNotFoundError: Resource (note/collection) not found

  • MemValidationError: Invalid request parameters

  • MemAPIError: General API errors

All errors are logged and returned with helpful context to the AI assistant.

๐Ÿ“š Examples

See the examples/ directory for complete usage examples:

  • basic_usage.py: Simple examples of each tool

  • advanced_usage.py: Complex workflows and patterns

๐Ÿค Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

๐Ÿ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

๐Ÿ”— Links

๐Ÿ’ก Use Cases

  • Meeting Notes: Automatically process and organize meeting transcripts

  • Research: Save and categorize research papers, articles, and findings

  • Customer Feedback: Collect and organize customer conversations

  • Knowledge Base: Build a searchable knowledge repository

  • Personal Memory: Keep track of ideas, thoughts, and learnings

๐Ÿ› Troubleshooting

Authentication Error

MemAuthenticationError: MEM_API_KEY environment variable or api_key parameter is required

Solution: Set your MEM_API_KEY in the .env file or environment.

Connection Timeout

httpx.ReadTimeout: timeout

Solution: Increase MEM_REQUEST_TIMEOUT in your .env file.

Invalid UUID

MemValidationError: invalid UUID format

Solution: Ensure note/collection IDs are valid UUIDs from Mem.ai.


Built with โค๏ธ using FastMCP and Mem.ai

-
security - not tested
A
license - permissive license
-
quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables AI assistants to intelligently save, organize, and retrieve content through Mem.ai's knowledge management platform. Supports creating notes, collections, and AI-powered content processing with automatic organization.

  1. โœจ Features
    1. ๐Ÿ“‹ Prerequisites
      1. ๐Ÿš€ Quick Start
        1. Installation
        2. Running the Server
      2. ๐Ÿ› ๏ธ Available Tools
        1. 1. mem_it - Intelligent Content Processing
        2. 2. create_note - Create Structured Note
        3. 3. read_note - Read Note
        4. 4. delete_note - Delete Note
        5. 5. create_collection - Create Collection
        6. 6. delete_collection - Delete Collection
      3. โš™๏ธ Configuration
        1. ๐Ÿ—๏ธ Architecture
          1. Key Components
        2. ๐Ÿงช Testing
          1. ๐Ÿ” Error Handling
            1. ๐Ÿ“š Examples
              1. ๐Ÿค Contributing
                1. ๐Ÿ“„ License
                  1. ๐Ÿ”— Links
                    1. ๐Ÿ’ก Use Cases
                      1. ๐Ÿ› Troubleshooting
                        1. Authentication Error
                        2. Connection Timeout
                        3. Invalid UUID

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