Modal MCP Server

local-only server

The server can only run on the client’s local machine because it depends on local resources.

Integrations

  • Serves as the API framework for the MCP server, providing documentation at /docs endpoint and RESTful endpoints for tool invocation.

  • Provides repository access for the MCP server code, supporting installation via git clone from a GitHub repository.

  • Provides tools for deploying and running Modal applications in the cloud, allowing AI agents to deploy Modal apps and execute functions within Modal applications.

Modal MCP Server

An MCP server implementation for interacting with Modal volumes and deploying Modal applications from within Cursor.

Installation

  1. Clone this repository:
git clone https://github.com/smehmood/modal-mcp-server.git cd modal-mcp-server
  1. Install dependencies using uv:
uv sync

Configuration

To use this MCP server in Cursor, add the following configuration to your ~/.cursor/mcp.json:

{ "mcpServers": { "modal-mcp-server": { "command": "uv", "args": [ "--project", "/path/to/modal-mcp-server", "run", "/path/to/modal-mcp-server/src/modal_mcp/server.py" ] } } }

Replace /path/to/modal-mcp-server with the absolute path to your cloned repository.

Requirements

  • Python 3.11 or higher
  • uv package manager
  • Modal CLI configured with valid credentials
  • For Modal deploy support:
    • Project being deployed must use uv for dependency management
    • Modal must be installed in the project's virtual environment

Supported Tools

  1. List Modal Volumes (list_modal_volumes)
    • Lists all Modal volumes in your environment
    • Returns JSON-formatted volume information
    • Parameters: None
  2. List Volume Contents (list_modal_volume_contents)
    • Lists files and directories in a Modal volume
    • Parameters:
      • volume_name: Name of the Modal volume
      • path: Path within volume (default: "/")
  3. Copy Files (copy_modal_volume_files)
    • Copies files within a Modal volume
    • Parameters:
      • volume_name: Name of the Modal volume
      • paths: List of paths where last path is destination
    • Example: ["source.txt", "dest.txt"] or ["file1.txt", "file2.txt", "dest_dir/"]
  4. Remove Files (remove_modal_volume_file)
    • Deletes a file or directory from a Modal volume
    • Parameters:
      • volume_name: Name of the Modal volume
      • remote_path: Path to file/directory to delete
      • recursive: Boolean flag for recursive deletion (default: false)
  5. Upload Files (put_modal_volume_file)
    • Uploads a file or directory to a Modal volume
    • Parameters:
      • volume_name: Name of the Modal volume
      • local_path: Path to local file/directory to upload
      • remote_path: Path in volume to upload to (default: "/")
      • force: Boolean flag to overwrite existing files (default: false)
  6. Download Files (get_modal_volume_file)
    • Downloads files from a Modal volume
    • Parameters:
      • volume_name: Name of the Modal volume
      • remote_path: Path to file/directory in volume to download
      • local_destination: Local path to save downloaded files (default: current directory)
      • force: Boolean flag to overwrite existing files (default: false)
    • Note: Use "-" as local_destination to write file contents to stdout
  1. Deploy Modal App (deploy_modal_app)
    • Deploys a Modal application
    • Parameters:
      • absolute_path_to_app: Absolute path to the Modal application file
    • Note: The project containing the Modal app must:
      • Use uv for dependency management
      • Have the modal CLI installed in its virtual environment

Response Format

All tools return responses in a standardized format, with slight variations depending on the operation type:

# JSON operations (list volumes, list contents): { "success": True, "data": {...} # JSON data from Modal CLI } # File operations (put, get, copy, remove): { "success": True, "message": "Operation successful message", "command": "executed command string", "stdout": "command output", # if any "stderr": "error output" # if any } # Error case (all operations): { "success": False, "error": "Error message describing what went wrong", "command": "executed command string", # for file operations "stdout": "command output", # if available "stderr": "error output" # if available }

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.

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

An MCP server that enables AI agents to interact with Modal, allowing them to deploy apps and run functions in a serverless cloud environment.

  1. Installation
    1. Configuration
      1. Requirements
        1. Supported Tools
          1. Modal Volume Operations
          2. Modal Deployment
        2. Response Format
          1. Contributing
            1. License