Express MCP Server – Text Extractor Overview

The Express MCP Server is a lightweight, stateless implementation of the Model Context Protocol (MCP) built on Express and TypeScript. It provides a minimal yet fully compliant MCP endpoint that can be integrated into any AI workflow requiring external data or simple computational logic. By exposing a standard JSON‑RPC interface, the server allows LLMs such as Claude to call tools, retrieve resources, or generate prompts without leaving the conversational context.

Solving the Integration Gap

Developers often need to augment an LLM’s capabilities with deterministic operations—such as echoing user input, validating data, or retrieving static content. Traditional approaches involve building custom REST APIs and writing bespoke adapters for each model. The Express MCP Server eliminates this boilerplate by offering a ready‑made, protocol‑aware bridge that follows the MCP specification. It handles request routing, streaming responses, and error handling out of the box, letting developers focus on business logic rather than protocol plumbing.

Core Functionality

• Stateless MCP Endpoint: The server listens on and processes JSON‑RPC calls, supporting the full MCP lifecycle (initialize, tool invocation, resource retrieval).
• Echo Tool: A simple tool that returns the supplied message, demonstrating how to expose custom logic. It can be extended to perform any synchronous or asynchronous operation.
• Echo Resource: The resource scheme returns the message directly as a resource payload, illustrating how to expose data via URIs.
• Echo Prompt: A prompt generator that creates a user-facing message, showing how to inject dynamic prompts into the model’s context.

All components are typed with TypeScript, ensuring compile‑time safety and clear API contracts. The server’s stateless nature means it can be scaled horizontally without session management concerns, making it suitable for cloud deployments.

Real‑World Use Cases

• Rapid Prototyping: Quickly add a tool to an LLM‑driven application without writing additional adapters. The echo example can be replaced with any function (e.g., date lookup, calculation).
• Data Retrieval: Expose internal data stores as resources () that the model can fetch on demand, enabling dynamic content injection.
• Prompt Engineering: Generate context‑specific prompts from the server, allowing developers to centralize prompt logic and keep models stateless.
• Testing & Debugging: Use the echo tool to verify that an LLM can correctly call external services and handle responses, serving as a sanity check during development.

Integration Workflow

1. Initialize: The client sends an JSON‑RPC request, registering the server’s capabilities (e.g., resource handling, sampling).
2. Tool Call: The model issues a request with the tool name and arguments. The server executes the corresponding function (echo) and streams the result back.
3. Resource Fetch: The model requests a resource URI; the server resolves it and returns the payload.
4. Prompt Injection: The model triggers a prompt generation, and the server supplies the formatted text for inclusion in the conversation.

Because the server adheres to the MCP specification, any compliant AI assistant can interact with it seamlessly. The Express framework ensures low overhead and easy deployment in Node.js environments, while TypeScript guarantees type safety across the entire stack.

This overview highlights how the Express MCP Server turns a simple echo example into a versatile, protocol‑compliant bridge for AI assistants, enabling developers to extend model capabilities with minimal friction.