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Documentation Index

Fetch the complete documentation index at: https://docs.karada.ai/llms.txt

Use this file to discover all available pages before exploring further.

Karada enables LLM-powered agents to safely interact with your proprietary systems. This playbook walks you through building an AI agent that uses a Karada-deployed MCP server to fetch data and perform actions on your behalf.

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Prerequisites

  • An existing REST API with an OpenAPI (v3) or Swagger specification.
  • A Karada account.
  • An AI client that supports the Model Context Protocol (e.g., Claude Desktop, Cursor).

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Phase 1: Ingesting your API

To let the AI understand your system, you must expose your API logic as discrete tools. Karada handles this automatically.
  1. Navigate to the Karada Dashboard and create a new Auto-MCP project.
  2. Provide your OpenAPI specification.
    • Tip: If you don’t have a spec file ready, you can paste a URL to your public openapi.json endpoint.
  3. Karada immediately generates a ready-to-deploy MCP server. Each endpoint from your spec is converted into a structured tool with a detailed description derived from your schema.

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Phase 2: Deploying the Server

Your generated server needs to run continuously so the agent can ping it anytime.
  1. Click Deploy from your project page.
  2. Karada provisions a secure, isolated microVM sandbox and boots the server.
  3. Once the build finishes, you receive a secure HTTPS URL. Copy this URL.
[!NOTE] Ensure your deployment has the necessary environment variables set (like API_KEY or DB_PASSWORD) if your target API requires authentication. The MCP server will automatically proxy these credentials.

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Phase 3: Connecting the Agent

Now you must configure your AI client to communicate with your Karada endpoint.

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Claude Desktop Configuration

  1. Open your Claude Desktop config file:
    • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add your Karada endpoint using the built-in MCP Server block:
{
  "mcpServers": {
    "my-company-api": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/client-cli", "connect", "https://your-karada-url.karada.ai/sse"],
      "env": {}
    }
  }
}
  1. Save the file and restart Claude Desktop.
  2. You should see a Tools (🛠️) icon in the input bar. Click it to verify your API endpoints are listed as available tools.

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Phase 4: Interacting with the Agent

Your agent is now fully connected. Try prompting it with tasks that require your API’s capabilities.
  • Example Prompt: “Look up the user ‘alice@example.com’ and tell me their subscription status.”
  • Example Prompt: “Fetch the latest 5 support tickets and summarize their issues.”
The agent will autonomously decide which tool to call, format the payload, send the request to your Karada MCP Server, and process the response!