Gorse MCP Server

The Agentum MCP server is a dedicated Model Context Protocol (MCP) implementation that plugs into a larger multi‑component system. It exposes a set of RESTful endpoints for tools, resources, and prompts while also offering Server‑Sent Events (SSE) streams so that AI assistants can receive real‑time updates. By centralizing these capabilities in a single, language‑agnostic service, developers can give Claude‑style agents instant access to external data sources and executable functions without having to write custom adapters for each tool.

At its core, the MCP server delivers three primary services: Tools, Resources, and Prompts. The Tools API allows agents to invoke arbitrary Python functions, turning the server into a lightweight compute engine that can perform calculations, query databases, or call third‑party APIs. Resources provide static assets—such as CSV files, images, or configuration bundles—that agents can download and reference during a conversation. Prompts are reusable templates that shape how an LLM generates text, enabling consistent formatting and context across multiple interactions. Together these services give developers a single point of control for all the contextual data an assistant might need, reducing latency and simplifying permission management.

The SSE endpoints are a standout feature: agents can subscribe to continuous streams of tool availability, resource updates, or prompt changes. This real‑time capability is crucial for dynamic workflows where new data arrives frequently—think stock market tickers, sensor feeds, or live customer support tickets. Instead of polling the server, an assistant receives push notifications whenever something relevant changes, ensuring that responses are always based on the latest information.

In practice, Agentum’s MCP server is ideal for any scenario that requires tight integration between an LLM and external systems. For example, a travel booking assistant can call a flight‑search tool via MCP, fetch pricing resources, and use a prompt template to format the itinerary. A data‑analysis bot can stream new datasets as resources, trigger a Python tool to compute statistics, and then generate a chart prompt. Because the server is built on FastAPI, it inherits robust validation, automatic OpenAPI documentation, and easy deployment in containerized environments.

Finally, the server’s dual‑path architecture—standard HTTP routes for one‑off calls and paths for streaming—offers flexibility in how agents consume data. Developers can choose the pattern that best fits their latency and bandwidth constraints, making Agentum a versatile component in any AI‑driven application stack.