AWO MCP Demo

Overview

This project demonstrates how the Model Context Protocol (MCP) can be used to expose processed AWO data to AI assistants such as Claude Desktop.

The prototype does not replace the existing AWO data pipeline. Instead, it provides a standardized interface between AI assistants and processed organizational data.

The current implementation uses a small CSV dataset as a demonstration. In a production environment, the same MCP server can be connected to the processed AWO database or data lake.

Related MCP server: CKAN MCP Server

Architecture

User
  │
Claude Desktop
  │
MCP Server
  │
Processed Dataset (CSV)

Production Architecture

User
  │
AI Assistant
  │
MCP Server
  │
Processed AWO Data
  │
Existing AWO Pipeline
(Scraping → Cleaning → Normalization → Deduplication)

Features

• MCP server implemented with FastMCP

• Facility search by city

• Facility search by service

• Facility counting

• Claude Desktop integration

• Interactive Python client for testing

Installation

Clone the repository.

git clone <repository-url>
cd awo-mcp-demo

Create and activate a Conda environment.

conda create -n awo-mcp python=3.11
conda activate awo-mcp

Install the required packages.

pip install -r requirements.txt

Running the MCP Server

Open a terminal in the project directory and activate the Conda environment.

conda activate awo-mcp
python server.py

The server will start and wait for incoming MCP requests. This is expected behavior. Leave this terminal open while testing.

Running the Demo Client

Open a second terminal in the same project directory.

Activate the Conda environment again.

conda activate awo-mcp
python client.py

The interactive menu will appear:

==============================
      AWO MCP Demo
==============================
1. Count facilities
2. Search by city
3. Search by service
0. Exit

Choose an option and enter the requested information. The client will communicate with the MCP server and display the returned results. for instance:

Option 1 – Count Facilities

Select 1 and enter a city name (e.g., Berlin).

The client calls the MCP tool:

count_facilities(city="Berlin")

The server searches the dataset and returns the number of matching facilities.

Example:

City: Berlin

Result
------
2

Claude Desktop Integration

Instead of using the demo client, you can connect the MCP server directly to Claude Desktop.

Step 1 – Install Claude Desktop

Download Claude Desktop:

https://claude.ai/download

Step 2 – Configure the MCP Server

Open the Claude Desktop configuration file and add the following server configuration.

{
  "mcpServers": {
    "awo": {
      "command": "C:\\Users\\<USERNAME>\\miniconda3\\envs\\awo-mcp\\python.exe",
      "args": [
        "D:\\path\\to\\awo-mcp-demo\\server.py"
      ]
    }
  }
}

Replace:

• <USERNAME> with your Windows username.

• D:\\path\\to\\awo-mcp-demo\\server.py with the full path to your server.py file.

Step 3 – Restart Claude Desktop

Save the configuration file and restart Claude Desktop.

The MCP server should appear under Settings → Developer → Local MCP Servers.

Once connected, Claude can automatically use the available MCP tools.

Example questions:

• How many AWO facilities are in Berlin?

• Show me all AWO facilities in Berlin.

• Find facilities that provide elderly care.

Example Questions

• How many AWO facilities are in Berlin?

• Show me all AWO facilities in Berlin.

• Find facilities that provide elderly care.

• Find childcare facilities.

Future Improvements

• Connect to the real processed AWO dataset

• Add additional MCP tools

• Connect to a production database

• Add authentication and authorization

• Improve logging and monitoring

• Containerize the application with Docker

License

MIT License