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Data Fairness

Data Fairness - the power of data analytics in Le Chat's one ping

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Core trick

Exposes data analytics operations as an MCP server that Le Chat calls for one-prompt analysis.

Tags

productivity

Description

Data Fairness - the power of data analytics in Le Chat's one ping
Mistral-MCP-Hackathon-Team-Rescuers/rescuers_bias_detection_server★ 00 forksPythonCreated Sep 13, 2025 · last push Sep 14, 2025

README excerpt

# mcp-server-template-python

A very simple Python template for building MCP servers using Streamable HTTP transport.

## Overview
This template provides a foundation for creating MCP servers that can communicate with AI assistants and other MCP clients. It includes a simple HTTP server implementation with example tools, resources & prompts to help you get started building your own MCP integrations.

## Prerequisites
- Install uv (https://docs.astral.sh/uv/getting-started/installation/)

## Installation

1. Clone the repository:

```bash
git clone git@github.com:alpic-ai/mcp-server-template-python.git
cd mcp-server-template-python
```

2. Install python version & dependencies:

```bash
uv python install
uv sync --locked
```

## Usage

Start the server on port 3000:

```bash
uv run main.py
```

## Running the Inspector

### Requirements
- Node.js: ^22.7.5

### Quick Start (UI mode)
To get up and running right away with the UI, just execute the following:
```bash
npx @modelcontextprotocol/inspector
```

The inspector server will start up and the UI will be accessible at http://localhost:6274.

You can test your server locally by selecting:
- Transport Type: Streamable HTTP
- URL: http://127.0.0.1:3000/mcp

## Development

### Adding New Tools

To add a new tool, modify `main.py`:

```python
@mcp.tool(
    title="Your Tool Name",
    description="Tool Description for the LLM",
)
async def new_tool(
    tool_param1: str = Field(description="The description of the param1 for the L

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