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fused-mcp

MCP.Pizza Chef: fusedio

Fused MCP is a server designed to facilitate the setup of MCP servers specifically for data scientists. It enables large language models (LLMs) like Claude to make HTTP requests and interact with external data sources and APIs, supporting advanced agent orchestration and real-time context integration. This server streamlines connecting LLMs to diverse data environments, enhancing AI workflows in data science applications.

Use This MCP server To

Enable LLMs to make HTTP requests for external data access Set up MCP servers tailored for data science workflows Orchestrate multiple AI agents for complex task execution Integrate real-time data sources into LLM-driven applications Facilitate secure and scoped model interactions with APIs Support multi-step reasoning across distributed data environments

README

Fused MCP Agents: Setting up MCP Servers for Data

  

Documentation   🌪️    Read the announcement    🔥    Join Discord

MCP servers allow LLMs like Claude to make HTTP requests, connecting them to APIs & executable code. We built this repo for ourselves & anyone working with data to easily pass any Python code directly to your own desktop Claude app.

This repo offers a simple step-by-step notebook workflow to setup MCP Servers with Claude's Desktop App, all in Python built on top of Fused User Defined Functions (UDFs).

Demo once setup

Requirements

If you're on Linux, the desktop app isn't available so we've made a simple client you can use to have it running locally too!

You do not need a Fused account to do any of this! All of this will be running on your local machine.

Installation

  • Clone this repo in any local directory, and navigate to the repo:

    git clone https://github.com/fusedio/fused-mcp.git
    cd fused-mcp/
  • Install uv if you don't have it:

    macOS / Linux:

    curl -LsSf https://astral.sh/uv/install.sh | sh

    Windows:

    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
    
  • Test out the client by asking for its info:

    uv run main.py -h
  • Start by following our getting-started notebook fused_mcp_agents.ipynb in your favorite local IDE to get set up and then make your way to the more advanced notebook to make your own Agents & functions

Notebook

Repository structure

This repo is build on top of MCP Server & Fused UDFs which are Python functions that can be run from anywhere.

Support & Community

Feel free to join our Discord server if you want some help getting unblocked!

Here are a few common steps to debug the setup:

  • Running uv run main.py -h should return something like this:

uv helper output function

  • You might need to pass global paths to some functions to the Claude_Desktop_Config.json. For example, by default we only pass uv:
{
    "mcpServers": {
        "qgis": {
            "command": "uv",
            "args": ["..."]
        }

    }
}

But you might need to pass the full path to uv, which you can simply pass to common.generate_local_mcp_config in the notebook:

# in fused_mcp_agents.ipynb
import shutil 

common.generate_local_mcp_config(
    config_path=PATH_TO_CLAUDE_CONFIG,
    agents_list = ["get_current_time"],
    repo_path= WORKING_DIR,
    uv_path=shutil.which('uv'),
)

Which would create a config like this:

{
    "mcpServers": {
        "qgis": {
            "command": "/Users/<YOUR_USERNAME>/.local/bin/uv",
            "args": ["..."]
        }

    }
}

Contribute

Feel free to open PRs to add your own UDFs to udfs/ so others can play around with them locally too!

Using a local Claude client (without Claude Desktop app)

If you are unable to install the Claude Desktop app (e.g., on Linux), we provide a small example local client interface to use Claude with the MCP server configured in this repo:

NOTE: You'll need an API key for Claude here as you won't use the Desktop App

  • Create an Anthropic Console Account

  • Create an Anthropic API Key

  • Create a .env:

    touch .env
  • Add your key as ANTHROPIC_API_KEY inside the .env:

    # .env
    ANTHROPIC_API_KEY = "your-key-here"
    
  • Start the MCP server:

    uv run main.py --agent get_current_time
  • In another terminal session, start the local client, pointing to the address of the server:

    uv run client.py http://localhost:8080/sse

fused-mcp FAQ

How do I install the fused-mcp server?
Installation instructions are available in the Fused documentation, typically involving cloning the repo and running setup scripts to deploy the MCP server environment.
Can fused-mcp connect LLMs to multiple data sources?
Yes, fused-mcp supports connecting LLMs to various HTTP endpoints and APIs, enabling rich data integration.
Is fused-mcp compatible with LLMs other than Claude?
While optimized for Claude, fused-mcp supports any LLM capable of HTTP interactions, including OpenAI and Gemini models.
How does fused-mcp ensure secure model interactions?
It implements scoped and observable protocols to control and monitor LLM requests, ensuring secure and compliant operations.
Can fused-mcp be used for real-time data workflows?
Yes, it supports real-time context feeding and multi-step reasoning for dynamic data environments.
Where can I get support or community help for fused-mcp?
You can join the Fused Discord community via the invite link in the documentation for support and collaboration.