Cortex MCP Server

Cortex: Declarative MCP Server for Go Developers

Cortex implements the full Model Context Protocol (MCP) specification, allowing developers to expose rich context—resources, tools, prompts, and sampling options—to large language models without entangling the model logic with application code. By separating context provisioning from LLM interaction, Cortex solves a common pain point: the difficulty of managing evolving toolsets and data sources in a single, maintainable server. With Cortex, the server becomes a lightweight, transport‑agnostic service that speaks JSON‑RPC over standard channels such as STDIO or Server‑Sent Events (SSE), keeping the LLM client focused on prompt construction and inference.

The server’s declarative API lets you define tools, resources, and prompts in plain Go structures. A tool can be a simple echo function or an advanced data‑retrieval operation, each annotated with JSON schema parameters. Resources expose static or dynamic datasets that the LLM can reference by name, while prompts provide pre‑formatted instruction sets. Cortex automatically handles the MCP lifecycle—registration, invocation, and error reporting—so developers spend less time writing boilerplate protocol handlers. Additionally, the server supports embedded integration; you can mount Cortex into existing HTTP servers or databases like PocketBase with minimal friction, turning any Go application into a fully‑featured MCP provider.

Key capabilities include:

• Transport flexibility – expose the same server over STDIO, SSE, or any custom protocol.
• Tool and resource discovery – clients receive a catalog of available operations, enabling dynamic UI generation or introspection.
• Prompt templating – pre‑defined prompts can be swapped or extended at runtime, facilitating rapid experimentation.
• Sampling controls – expose temperature, top‑k, and other generation parameters directly through the MCP interface.

Real‑world use cases abound: a data analytics platform can expose query tools as MCP operations, allowing an LLM to generate natural‑language queries that are executed on the backend. A customer support system can expose ticket‑lookup resources, letting the assistant retrieve context before drafting responses. In research environments, a lab can publish experimental tools and datasets through Cortex, enabling collaborative model development without exposing raw code.

By adhering to the MCP spec and Go best practices, Cortex offers a clean architecture that scales from simple prototypes to production‑grade services. Its declarative design reduces cognitive load, while transport agnosticism ensures that the same server logic works across different client implementations—be it a local command‑line tool or a cloud‑hosted assistant. For developers looking to integrate LLMs with custom tooling and data, Cortex provides a robust, future‑proof foundation.