Modal MCP Toolbox

What is modal-mcp-toolbox

modal-mcp-toolbox is a versatile suite of tools developed by philipp-eisen to enhance the abilities of language models through the Model Context Protocol (MCP). By running on the Modal platform, this toolbox extends the functionality of language learning models (LLMs) in various applications such as Goose or the Claude Desktop App. The primary aim of modal-mcp-toolbox is to provide a seamless integration of MCP tools, enabling AI systems to interact securely with external data sources, tools, and prompts.

How to Use modal-mcp-toolbox

To begin using modal-mcp-toolbox, you will need a Modal account and a client that supports MCP, such as the Claude Desktop App or Goose. Here's a step-by-step guide on how to set it up:

1. Prerequisites:

   • Ensure you have a Modal account and the Modal CLI configured.
   • Install UV, a necessary component for running MCP commands.
   • Use an MCP-compatible client like Claude or Goose.

2. Installation:

   • Claude Desktop App:

     • Navigate to Settings > Developer and select 'Edit Config'.

     • Add the following configuration to connect to the MCP server:

       {
         "mcpServers": {
           "modal-toolbox": {
             "command": "uvx",
             "args": ["modal-mcp-toolbox"]
           }
         }
       }
       

   • Goose:

     • Go to Settings and choose 'Add' to configure a new extension.
     • Set the command to uvx modal-mcp-toolbox.

3. Execution:

   • With the above configurations, you can execute MCP commands within your chosen client, leveraging the powerful tools provided by modal-mcp-toolbox.

Key Features of modal-mcp-toolbox

modal-mcp-toolbox offers a suite of tools designed to enhance AI capabilities through secure and efficient execution of tasks. Here are the key features:

• run_python_code_in_sandbox: Execute Python code in a controlled, sandboxed environment. This feature ensures that code runs securely without affecting the host system, making it ideal for testing and experimentation.

• generate_flux_image: Utilize the FLUX model to create images. This tool enables AI systems to generate visual content, expanding their creative and functional possibilities.

These tools are designed to integrate seamlessly with MCP, allowing AI systems to access real-time data and perform complex tasks with ease. Whether you are developing AI applications or enhancing existing systems, modal-mcp-toolbox provides the necessary resources to extend the functionality of your language models in a secure and efficient manner.