Server (MCP Server)

An MCP Server is a lightweight service that implements the Model Context Protocol to expose specific capabilities to AI models. It acts as the "bridge" between the protocol and the actual data or API.

Core Responsibilities

• Exposing Tools: Defining exactly what functions an AI can call (e.g., lookup_customer, send_slack_message).
• Providing Resources: Serving files or data chunks that provide context to the model.
• Executing Actions: Translating JSON-RPC requests from the client into actual API calls or database queries.

MCP servers can be written in almost any language, including Go, TypeScript, Python, and Rust. They can run locally on a developer's machine or be hosted in the cloud.

Questions & Answers

What is the role of an "MCP Server" in an AI application?

An MCP Server acts as a lightweight "bridge" between the protocol and actual data sources or APIs. It translates standardized AI requests into specific actions, such as querying a database or fetching a file.

What are the three core responsibilities of an MCP server?

A server is responsible for: 1) Exposing tools (functions the AI can call), 2) Providing resources (static data chunks), and 3) Executing actions (translating JSON-RPC requests into actual system calls).

In which programming languages can an MCP server be implemented?

MCP servers are technology-agnostic. They can be implemented in any modern programming language, with official and community support available for Go, TypeScript, Python, and Rust.