Model Context Protocol Registry

Connect LLMs to the Real World

Explore production-ready MCP servers, generate configuration files, and access the best learning resources to build intelligent agent workflows.

🔍

Explore Servers

Config Builder

Interactive mcp_config.json Builder

Select the tools you want to use, customize their configuration arguments, and copy the unified configuration file directly into your agent setup.

1. Select & Configure Servers

2. Live config file

// Configuration will generate here...

💡 How to Use:

Save this code as mcp_config.json in your local app configuration directory (e.g., ~/Library/Application Support/Antigravity/mcp_config.json or your Claude Desktop config folder).

Developer Guide

Build Your Own MCP Server

Writing custom MCP servers is simple. Create integrations in Node.js or Python to give your AI workspace access to proprietary systems and APIs.

1
Install the SDK: Start by installing the model-context-protocol client/server library.
2
Define Tools: Register tool functions, descriptions, and parameter schemas.
3
Expose Endpoints: Read/write inputs over stdio or HTTP endpoints.
server.js (Node.js SDK) 
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

// Initialize server
const server = new Server({
  name: "custom-mcp-server",
  version: "1.0.0"
}, {
  capabilities: { tools: {} }
});

// Define a tool
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{
    name: "calculate_metric",
    description: "Computes custom business analytics",
    inputSchema: {
      type: "object",
      properties: {
        id: { type: "string" }
      },
      required: ["id"]
    }
  }]
}));

// Connect standard input/output transport
const transport = new StdioServerTransport();
await server.connect(transport);
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Paste your mcp_config.json

Validation Report

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Waiting for input... Paste your configuration JSON in the editor on the left to begin audit.
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