Intent MCP Server

The Intent MCP Server is a dedicated Model Context Protocol (MCP) service that turns free‑form natural language into structured, actionable intent objects. By exposing a clean REST API for intent creation, retrieval, and processing, it removes the burden of building custom NLU pipelines from developers who need to embed AI assistants into their applications. The server’s core value lies in its ability to translate ambiguous user requests into well‑defined goals, constraints, and metadata that downstream services can consume directly.

At its heart, the server implements a robust intent‑processing workflow. Incoming raw intents are parsed and decomposed into a typed model that includes goals, constraints, and any extracted metadata. This transformation is performed by a dedicated service layer that can be swapped or extended without touching the HTTP interface. The design prioritizes type safety, with a full TypeScript implementation that guarantees contract consistency across the API, storage layer, and business logic. Developers can rely on a predictable data shape when integrating the server into larger AI workflows, such as orchestrating multi‑step actions or feeding intents to downstream planning engines.

Key capabilities include:

• Intent creation and retrieval through intuitive endpoints (, ).
• Natural language understanding that converts raw text into structured goals and constraints.
• Pluggable storage, offering an in‑memory baseline while allowing custom persistence adapters (e.g., databases, caches).
• Comprehensive error handling and logging, ensuring that failures are traceable and recoverable.
• Extensible architecture with clear separation of controllers, services, storage, and models, facilitating unit testing and future feature addition.

Typical use cases span a wide range of AI‑enabled applications. A chatbot might submit user utterances to the server, receive a structured intent, and then route that intent to specific microservices for booking, data retrieval, or content generation. Workflow engines can consume the processed intent to trigger complex business processes, while analytics platforms can ingest the structured data for monitoring user behavior. Because the server exposes a standard MCP interface, any AI assistant that understands MCP can seamlessly integrate with it, enabling rapid prototyping and deployment of intent‑driven features.

In summary, the Intent MCP Server offers developers a reliable, type‑safe bridge between natural language input and executable workflows. Its modular design, coupled with a focus on maintainability and extensibility, makes it an ideal foundation for building sophisticated AI assistants that require precise intent interpretation without reinventing core NLU components.