Core MCP Server: The Heart of the MCP Ecosystem for AI Assistants

What is the Core MCP Server?
The Core MCP Server is the base server that powers the MCP (Model Context Protocol). It is a reference implementation developed by AWS to demonstrate how an MCP server can expose functions and documents to language models (LLMs) following the MCP standard.
Unlike other more specialized servers, the Core MCP Server is not tied to a specific AWS technology or service. Its goal is to be a generic and modular framework that allows for custom integrations, testing, or prototyping.
What is it used for?
This server serves multiple key roles:
● MCP Protocol Demonstration: Ideal for those who want to understand how MCP works in practice.

● Local Development: Allows you to set up an MCP test environment without the need for cloud services.

● Tool Prototyping: You can define new tools, workflows, or integrations without setting up additional infrastructure.

● Foundation for Other Servers: Many more advanced MCP Servers are built upon this reusable core.

What does it offer?
The Core MCP Server includes the following components:
● Tool Providers: Code that implements functions exposed to the model. For example, calculation functions, data queries, or response generation.

● Document Providers: Mechanisms to expose documents to LLMs as if they were part of their context. Useful for RAG (retrieval-augmented generation).

● HTTP Server: Runs locally and allows connections from compatible assistants (such as Cursor, cline, or Windsurf).

● Declarative Configuration: The available tools and documents are defined in YAML configuration files.

How is it used?
The typical workflow for using the Core MCP Server follows these steps:
1. Clone the repository and compile the server with Rust (use cargo).

2. Define tools in a YAML file, specifying their name, description, and expected arguments.

3. Run the server locally.

4. Connect an MCP client (e.g., cline or Cursor) and observe how the model starts invoking functions and querying documents as part of the chat.

This workflow makes it ideal for labs, training, experimentation, or internal development of enterprise assistants.
What makes it special?
● Open-source, clean, and extensible code.

● Developer-oriented, doesn’t require AWS infrastructure.

● Educational model, perfect for understanding architectures designed for LLMs.

● Allows complete customization, ideal for corporate or private integrations.

Recommended Use Cases
● Create a prototype of an internal co-pilot that accesses shared files.

● Simulate how a model would interact with internal systems before moving to production.

● Build a training lab for technical teams on applied AI.

● Evaluate how an LLM would respond to different document contexts or tools before implementing the real assistant.

Conclusion
The Core MCP Server is the foundational piece that enables understanding and building with the MCP Protocol. Its modular design and cloud-free dependencies make it a powerful tool for developers, researchers, and solution architects working with AI-based systems.
In a world where LLMs need to stop being black boxes and become real business tools, this server offers the most accessible and transparent entry point into the MCP ecosystem.