# mcp-mem.ai
## 基本信息
- Slug: `burtthecoder-mcp-mem.ai`
- Source: modelscope
- Publisher: @BurtTheCoder/mcp-mem.ai
- Categories: note-taking / knowledge-and-memory / workplace-and-productivity
- Hosted: No
- License: MIT License
- Source URL: https://www.modelscope.cn/mcp/servers/@BurtTheCoder/mcp-mem.ai
## 简介
暂无描述。
## 安装提示

```bash
git clone https://github.com/yourusername/mcp-mem.ai.git cd mcp-mem.ai
```

## MCP Server 详情

# MCP Server for Mem.ai

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

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

##  Features

-  **Intelligent Memory**: Save and process content with Mem It's AI-powered organization
-  **Note Management**: Create, read, and delete structured markdown notes
-  **Collections**: Organize notes into searchable collections
-  **Type-Safe**: Full type hints and Pydantic validation
-  **Async/Await**: High-performance async I/O throughout
-  **Clean API**: Simple, intuitive interface for AI assistants
-  **Production-Ready**: Comprehensive error handling and logging
-  **Well-Tested**: Full test suite with pytest

##  Prerequisites

- Python 3.10 or higher
- A [Mem.ai](https://mem.ai) account
- Mem.ai API key ([get one here](https://mem.ai))

##  Quick Start

### Installation

1. Clone the repository:
```bash
git clone https://github.com/yourusername/mcp-mem.ai.git
cd mcp-mem.ai
```

2. Install dependencies:
```bash
pip install -e .
```

3. Set up your environment:
```bash
cp .env.example .env
# Edit .env and add your MEM_API_KEY
```

### Running the Server

#### Local Development
```bash
fastmcp run src/mcp_mem/server.py
```

#### Using with Claude Desktop

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

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

#### Using with Other MCP Clients

```python
from mcp_mem import mcp

# Run the server
mcp.run()
```

##  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:**
```python
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:**
```python
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:**
```python
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:**
```python
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:**
```python
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:**
```python
delete_collection("5e29c8a2-c73b-476b-9311-e2579712d4b1")
```

##  Configuration

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

```bash
# 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
```

##  Architecture

```
src/mcp_mem/
 __init__.py      # Package initialization
 models.py        # Pydantic data models
 client.py        # Mem.ai API client
 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

##  Testing

Run the test suite:

```bash
# 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
```

##  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.

##  Examples

See the `examples/` directory for complete usage examples:

- `basic_usage.py`: Simple examples of each tool
- `advanced_usage.py`: Complex workflows and patterns

##  Contributing

Contributions are welcome! Please feel free to submit a …