Simple JSON MCP Server

Overview

The Node Simple MCP Example demonstrates how to expose a lightweight, local JSON HTTP server as an MCP (Model Context Protocol) service that Claude can call directly from its desktop interface. By bridging a familiar Node.js runtime with the MCP tooling system, developers can prototype and test AI‑driven interactions against a controllable data source without leaving the Claude environment. This setup is especially useful for rapid iteration, debugging, or learning how MCP tools translate natural language into concrete API calls.

At its core, the server registers two MCP tools: one for retrieving all posts () and another for inserting a new post with specified attributes. When Claude receives a user prompt that matches one of these commands, the MCP client translates it into an HTTP request against the local mock JSON server. The response is then fed back to Claude, allowing the assistant to present data or confirm actions in a natural conversation. This pattern removes the need for separate client code, enabling developers to focus on modeling interactions rather than plumbing.

Key capabilities of this example include:

• Tool registration via a simple JSON configuration that points to the compiled MCP server script.
• Automatic discovery of resources when the MCP server starts, visible at .
• Bidirectional communication where Claude can both query and mutate data, illustrating full CRUD support.
• Minimal runtime dependencies, requiring only Node.js and the MCP inspector for debugging.

Typical use cases span from educational demos—showing students how language models can orchestrate API calls—to internal tooling where a small team needs a quick, local data store that the AI can manipulate. Because the server runs locally, latency is low and no external network exposure is needed, making it ideal for testing or prototyping before scaling to production services.

Integration into AI workflows is straightforward: once the MCP server is running and configured in Claude’s desktop settings, any conversation can invoke these tools simply by phrasing a request. The assistant automatically maps the natural language to the appropriate HTTP endpoint, handles serialization/deserialization, and returns results in conversational form. This seamless bridge between human intent and machine action exemplifies the power of MCP for building sophisticated, context‑aware AI assistants.