Getting Started

Transform your API into an AI-ready MCP service in 60 seconds.

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: Upload to MCPify

Upload your configuration to the MCPify gateway. You can do this through our web interface or using the API directly.

Via Web Interface: Go to dashboard.mcpify.org and upload your config

Via API: POST to /services endpoint with your configuration

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.pernille.ai/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 90%+ cache hit rate

✅ 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

19 tools for JSON manipulation and exploration

Next Steps