This workflow transforms the BikeWise API v2 into an MCP-compatible interface tailored for AI agents. Its primary goal is to facilitate seamless access to biking incident and location data through webhook triggers and HTTP requests, enabling AI systems to retrieve, process, and visualize incident reports and geographic information efficiently.

The workflow begins with a ‘BikeWise API v2 MCP Server’ trigger node, which acts as the webhook endpoint listening for requests from AI agents. Once triggered, the workflow can execute multiple HTTP request nodes to interact with the BikeWise API, fetching incident data and location information based on parameters supplied by the AI agents via `$fromAI()` expressions. These parameters include incident types, date ranges, geographic proximity, and full-text queries.

Specifically, the nodes handle:

– Paginated incident retrieval with filters for incident type, date, and proximity.

– Fetching unpaginated GeoJSON data for incidents within specified parameters.

– Retrieving location data with various spatial filters, including limits and full data sets.

The nodes are interconnected such that the MCP trigger initiates the data fetch operations, which are then processed or returned directly to the AI system. Commented sticky notes guide users through setup, usage, and customization.

This workflow is particularly useful for developers integrating real-time incident and location data into AI-powered applications, such as monitoring biking safety, visualizing incident maps, or conducting analytical reviews based on geographic and temporal filters.