Getting Started

What it takes to turn your API into an AI-ready MCP service.

Prerequisites

  • API credentials for the service you want to MCPify
  • Basic understanding of your API's endpoints
  • Access to MCPify gateway (request access if needed)

Step 1: Create Your Configuration

Create a JSON configuration file that describes your API. This tells MCPify how to interact with your service.

{
  "service_name": "your-api",
  "base_url": "https://api.your-service.com/v1",
  "auth_type": "bearer",
  "tools": {
    "test_connection": {
      "description": "GET /status - Test API connection\n\nVerifies credentials and connectivity",
      "endpoint": "/status",
      "method": "GET"
    },
    "list_items": {
      "description": "GET /items - List all items with pagination",
      "endpoint": "/items",
      "method": "GET",
      "input_schema": {
        "type": "object",
        "properties": {
          "limit": {"type": "integer", "default": 10},
          "offset": {"type": "integer", "default": 0}
        }
      }
    }
  }
}

Step 2: Send it to us

MCPify is a hosted gateway — we register the service against your configuration rather than you deploying anything.

Start here: tell us about the API and send the configuration with it.

Credentials: once the integration is agreed, submit them at credentials.mcpify.org — they go to Google Secret Manager, never into the config file.

Step 3: Test Your MCPified API

Your API is now available as an MCP service! Test it using the MCP protocol:

# Test your MCPified API
curl -X POST https://your-api.mcp.mcpify.org/mcp \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-gateway-api-key" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/list",
    "id": 1
  }'

Expected response:

{
  "jsonrpc": "2.0",
  "result": {
    "tools": [
      {
        "name": "test_connection",
        "description": "Test API connection and verify credentials",
        "inputSchema": {"type": "object", "properties": {}}
      },
      {
        "name": "list_items",
        "description": "List all items with pagination",
        "inputSchema": {
          "type": "object",
          "properties": {
            "limit": {"type": "integer", "default": 10},
            "offset": {"type": "integer", "default": 0}
          }
        }
      }
    ]
  },
  "id": 1
}

Step 4: Connect to AI Assistants

Your MCPified API can now be used with any MCP-compatible AI assistant:

Claude Desktop

Add your service URL to Claude's MCP configuration file

Custom Integration

Use the MCP client library to integrate with your own applications

What's Included

When you MCPify an API, you automatically get:

✅ Token Counting

Automatic token counting, with responses cached per tool

✅ Smart Caching

Response caching with field filtering support

✅ OAuth Management

Secure token storage with automatic refresh

✅ Rate Limiting

Built-in protection against API abuse

✅ Analytics

Usage tracking and performance metrics

✅ Data Navigation

Tools for JSON manipulation and exploration

Next Steps