What is Mage Mcp Server

mage-mcp-server is a comprehensive Model Context Protocol (MCP) integration for Mage.ai, enabling AI assistants like Anthropic’s Claude to efficiently interact with Mage data pipelines.

Use cases

Use cases include managing data pipelines for machine learning projects, facilitating AI model training, and enabling efficient data manipulation and analysis workflows.

How to use

To use mage-mcp-server, clone the repository, install dependencies, configure environment variables, and run the server using the provided scripts. You can also install it directly with Claude Desktop for an enhanced experience.

Key features

Key features include browsing pipelines, viewing and editing code, executing pipelines or individual blocks, searching for blocks by name or type, and creating new pipelines and blocks from scratch.

Where to use

mage-mcp-server can be used in data science, machine learning, and AI development environments where integration with Mage.ai data pipelines is required.

Mage.ai MCP Integration

A comprehensive Model Context Protocol (MCP) integration for Mage.ai, allowing AI assistants like Anthropic’s Claude to efficiently work with Mage data pipelines.

Features

• Browse Pipelines: Explore all Mage.ai pipelines and their components
• View and Edit Code: Read and modify block code
• Execute Pipelines: Run entire pipelines or individual blocks
• Search Functionality: Find blocks by name, type, or content
• Pipeline Creation: Create new pipelines and blocks from scratch

Installation

# Clone the repository
git clone https://github.com/yourusername/mage-mcp.git
cd mage-mcp

# Install dependencies
pip install -r requirements.txt

Configuration

The server can be configured using environment variables or command-line arguments:

• MAGE_API_URL: URL for the Mage.ai API (default: http://localhost:6789/api/)
• MAGE_API_KEY: API key for authentication (if required)

Create a .env file for persistent configuration:

MAGE_API_URL=http://localhost:6789/api/
MAGE_API_KEY=your_api_key_if_needed

Usage

Running the Server

Use the provided script to start the MCP server:

# Using stdio transport (default)
python run_server.py

# Using SSE transport
python run_server.py --transport sse --host localhost --port 3000

# Additional options
python run_server.py --api-url http://your-mage-instance:6789/api/ --api-key your_key --debug

Installing with Claude Desktop

For the best experience, install this server directly with Claude Desktop:

# Using mcp CLI
mcp install run_server.py --name "Mage.ai Manager"

# With environment variables
mcp install run_server.py -v MAGE_API_URL=http://localhost:6789/api/ -v MAGE_API_KEY=your_key

Development Mode

For testing and development:

mcp dev run_server.py

MCP Resources and Tools

Resources

• mage://pipelines - List all pipelines
• mage://pipelines/{pipeline_uuid} - Get pipeline details
• mage://pipelines/{pipeline_uuid}/blocks - List blocks in a pipeline
• mage://pipelines/{pipeline_uuid}/blocks/{block_uuid} - Get block details
• mage://pipelines/{pipeline_uuid}/blocks/{block_uuid}/content - Get block code
• mage://pipelines/{pipeline_uuid}/content - Get entire pipeline content
• mage://pipelines/{pipeline_uuid}/execution - Get pipeline execution details
• mage://blocks/search/{query} - Search blocks by name, type or content

Tools

• list_all_pipelines() - List all pipelines with details
• get_pipeline_details(pipeline_uuid) - Get pipeline information
• list_pipeline_blocks(pipeline_uuid) - List blocks in a pipeline
• get_block_content(pipeline_uuid, block_uuid) - Get block code
• create_pipeline(name, description, pipeline_type) - Create a new pipeline
• create_block(pipeline_uuid, name, block_type, language, content, upstream_blocks) - Create a block
• update_block_content(pipeline_uuid, block_uuid, content) - Update block code
• execute_pipeline(pipeline_uuid) - Run a pipeline
• execute_block(pipeline_uuid, block_uuid) - Run a specific block
• get_pipeline_code(pipeline_uuid) - Get complete pipeline code

Example Interactions

User: Show me all the pipelines in Mage.ai.
Claude: Let me check what pipelines are available in your Mage.ai instance.

[Lists all pipelines with descriptions and types]

User: Can you show me the blocks in the data_ingestion pipeline?
Claude: Here are all the blocks in the data_ingestion pipeline:

[Lists all blocks with their types, languages, and status]

User: I need to modify the 'transform_data' block to fix a bug.
Claude: Let me help you with that. First, let's look at the current code:

[Shows the current block code]

Here's how we can fix the bug:
[Explains the issue and suggests changes]

Would you like me to update the block with these changes?

User: Yes, please update it.
Claude: I've updated the block. Here's the new content:

[Shows the updated code]

The changes have been saved successfully.

Contributing

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

License

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