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Fused MCP Agents: Streamline Servers, Amplify Insights - MCP Implementation

Fused MCP Agents: Streamline Servers, Amplify Insights

Empower data workflows with Fused MCP Agents—streamline server setups, boost scalability, and focus on insights, not infrastructure. Your data science team’s new secret weapon.

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About Fused MCP Agents

What is Fused MCP Agents: Streamline Servers, Amplify Insights?

Fused MCP Agents is a tool designed to seamlessly integrate Large Language Models (LLMs) like Claude with external APIs and code execution environments. It simplifies the process of deploying and managing servers, enabling developers to focus on building solutions rather than infrastructure. By leveraging the Model Control Protocol (MCP), this framework allows LLMs to interact with external resources securely and efficiently, all while running locally on your machine without requiring cloud dependencies.

How to Use Fused MCP Agents: Streamline Servers, Amplify Insights?

To utilize Fused MCP Agents, start by installing Python and cloning the repository. Configure your environment using provided notebooks and run commands to initialize the server. The process supports cross-platform setups, including Linux alternatives via API keys. Detailed steps are outlined in documentation, with troubleshooting guides and community resources available for seamless implementation.

Fused MCP Agents Features

Key Features of Fused MCP Agents: Streamline Servers, Amplify Insights?

  • Local Execution: Run servers directly on your machine without cloud infrastructure.
  • Cross-Platform Support: Works on Windows, macOS, and Linux with tailored configurations.
  • No Account Required: No sign-up needed for basic usage; API keys are optional for extended features.
  • Flexible Configuration: Customizable agents and workflows via YAML configurations.
  • Community-Driven: Access forums, GitHub issues, and official documentation for support.

Use Cases of Fused MCP Agents: Streamline Servers, Amplify Insights?

Applications include:

  • Data analysis automation by connecting LLMs to SQL databases.
  • Building chatbots that interface with custom APIs for real-time data retrieval.
  • Testing and prototyping new LLM use cases locally before deployment.
  • Enabling LLM-driven workflows in restricted environments without internet access.

Fused MCP Agents FAQ

FAQ from Fused MCP Agents: Streamline Servers, Amplify Insights?

  • How do I troubleshoot server issues? Check logs for error codes and refer to the official guide.
  • Can I use this on Linux? Yes, via the Anthropic API with an API key.
  • Where do I report bugs? Submit issues via GitHub or the Discord community.
  • How do I contribute code? Fork the repository, implement features, and submit pull requests following the contribution guidelines.

Content

Fused MCP Agents: Setting up MCP Servers for Data

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"

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
    

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