Fledge MCP Server

Fledge MCP Server Overview

The Fledge MCP server bridges the gap between an on‑premises or cloud‑hosted Fledge instance and AI assistants that speak the Model Context Protocol (MCP). By exposing a rich set of tools—ranging from sensor data retrieval to UI component generation—the server lets conversational agents issue natural‑language commands that translate directly into Fledge API calls. This eliminates the need for developers to write boilerplate code or maintain custom SDKs, allowing them to focus on higher‑level logic and rapid prototyping.

At its core, the server solves two practical problems. First, it abstracts Fledge’s RESTful API into a uniform tool set that can be invoked from any MCP‑compliant client, such as Cursor AI. Second, it provides a secure, authenticated entry point for production deployments through an optional API‑key mechanism. The health endpoint confirms that the server is reachable and that Fledge itself is running, giving developers immediate feedback during integration.

Key capabilities include:

• Data Access & Management – Retrieve sensor readings, list sensors, or ingest synthetic data for testing.
• Service Control – Query the status of Fledge services, start or stop them on demand, and tweak configuration parameters.
• Frontend Generation – Ask the AI to produce React components or sample templates for various frameworks, streamlining dashboard creation.
• Real‑Time Streaming – Subscribe to live sensor updates or fetch the latest reading on demand.
• Debugging & Validation – Verify API connectivity, simulate frontend requests, and explore the full Fledge schema.
• Advanced AI Assistance – Generate realistic mock data or obtain UI improvement suggestions, turning the server into a creative partner.

Typical use cases span industrial IoT monitoring, rapid prototype dashboards, and automated testing pipelines. For example, a data scientist can ask the AI to “generate a line chart for temperature sensor temp1” and immediately receive a React component that can be dropped into an existing application. An operations engineer might instruct the assistant to “start the data ingestion service” and have it toggle the underlying Fledge service without touching the command line.

Integration into AI workflows is straightforward: once the MCP server URL and tool file are registered in a client such as Cursor, any prompt that references one of the exposed tools will be translated into an HTTP POST to . The server’s clear, self‑documenting tool definitions allow the AI to surface relevant options automatically, making the assistant feel like a natural extension of the developer’s IDE. The secure variant further protects sensitive deployments by requiring an API key, ensuring that only authorized agents can invoke powerful configuration or ingestion operations.

In summary, the Fledge MCP server empowers developers to harness the full power of Fledge through conversational AI, reducing friction, accelerating delivery, and enabling a new class of interactive, data‑centric applications.