This n8n workflow enables seamless integration of the Czech Republic’s Mobility API with AI agents through an MCP (Multi-Channel Protocol) server. Its purpose is to provide real-time mobility data concerning population movement between locations using mobile network data, which can be used for transportation planning, urban analysis, or AI-driven decision-making.
### Workflow Breakdown:
1. **Setup Instructions Note**: Provides detailed steps to import, configure, and activate the workflow, including how to connect it with AI agents via webhook URL.
2. **Workflow Overview Note**: Explains the main functions, which include converting the Mobility API into an MCP-compatible interface, and describes the API endpoints available, such as retrieving application info and calculating transit routes.
3. **MCP Trigger Node**: Acts as the main endpoint, listening for requests from AI agents.
4. **HTTP Request Nodes**:
– _Retrieve Application Info_: Calls the Mobility API to fetch version and data info.
– _Calculate Transit Route_: Calculates traffic flow between two locations, with parameters for source, destination, and type of occurrence (transit, visit, or both).
– These nodes utilize `$fromAI()` expressions to dynamically populate parameters based on incoming requests.
5. **Connection Logic**: The MCP trigger activates the data fetching or transit calculation nodes depending on the request, responding directly to the AI agent with the API’s data.
### Use Cases:
This workflow is ideal for developers or data analysts who need to incorporate real-time mobility insights into AI-powered applications, Smart City dashboards, or transportation planning tools. It simplifies the process by providing a RESTful interface to the Mobility API, enabling automation and data-driven decision-making.
### Customization and Expansion:
You can add data transformation, error handling, and logging nodes as needed, and modify parameters or endpoints for expanded API capabilities.
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