Build an AI Marketing Agent with Claude MCP and Kettio

async function generateAndOptimize(apiKey, prompt, audience, goal, iterations = 3) {
  let bestScore = 0;
  let bestAssetUrl = null;

  for (let i = 0; i < iterations; i++) {
    console.log(`
--- Iteration ${i + 1} ---`);

    // Generate new creative
    const genRes = await fetch('https://kettio.com/api/mcp/generate', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        prompt: i === 0
          ? prompt
          : `${prompt}. Improve on the previous version: make the value proposition clearer and the visual hierarchy stronger.`,
        count: 3,
        model: 'nano-banana-pro'
      })
    });
    const { jobId } = await genRes.json();

    // Poll until complete
    let assets = [];
    while (true) {
      const statusRes = await fetch(`https://kettio.com/api/mcp/jobs/${jobId}`, {
        headers: { 'Authorization': `Bearer ${apiKey}` }
      });
      const status = await statusRes.json();

      if (status.status === 'completed') {
        assets = status.assets;
        break;
      }
      if (status.status === 'failed') throw new Error('Generation failed');
      await new Promise(r => setTimeout(r, 5000));
    }

    // Score all generated variants
    const rankRes = await fetch('https://kettio.com/api/v1/rank', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        assets: assets.map((a, idx) => ({
          url: a.downloadUrl,
          id: `iter${i}-var${idx}`
        })),
        audience,
        goal
      })
    });
    const { ranked } = await rankRes.json();

    const topVariant = ranked[0];
    console.log(`Best this round: ${topVariant.score.toFixed(2)} (${topVariant.asset_id})`);

    if (topVariant.score > bestScore) {
      bestScore = topVariant.score;
      bestAssetUrl = topVariant.asset_url;
      console.log(`New overall best: ${bestScore.toFixed(2)}`);
    }

    // If we've hit a good enough score, stop early
    if (bestScore >= 4.0) {
      console.log('Score threshold reached. Stopping.');
      break;
    }
  }

  return { bestScore, bestAssetUrl };
}

MCP vs. REST API: Choosing the Right Approach

Agent Framework Integration

If you're building agents with frameworks like LangChain, CrewAI, or the Anthropic Agent SDK, Kettio endpoints work as standard tool definitions:

# Example: LangChain tool definition
from langchain.tools import tool
import requests

@tool
def score_creatives(
    image_urls: list[str],
    audience_name: str,
    audience_demographics: dict,
    goal: str = "purchase-intent"
) -> dict:
    """Score ad creatives against a target audience.
    Returns ranked results with scores, rationales, and confidence levels."""
    response = requests.post(
        "https://kettio.com/api/v1/rank",
        headers={
            "Authorization": f"Bearer {KETTIO_API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "assets": [{"url": url, "id": f"ad-{i}"} for i, url in enumerate(image_urls)],
            "audience": {
                "name": audience_name,
                "demographics": audience_demographics
            },
            "goal": goal
        }
    )
    return response.json()

# Example: Anthropic Agent SDK tool
from anthropic import Anthropic

client = Anthropic()

tools = [{
    "name": "rank_creatives",
    "description": "Score and rank ad creatives against a target audience using Kettio's SSR pipeline. Returns ranked results with scores (1-5), natural language rationales, and confidence levels.",
    "input_schema": {
        "type": "object",
        "properties": {
            "assets": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "url": {"type": "string"},
                        "id": {"type": "string"}
                    },
                    "required": ["url"]
                },
                "description": "1-20 image URLs to score"
            },
            "audience": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "demographics": {"type": "object"}
                },
                "required": ["name"]
            },
            "goal": {
                "type": "string",
                "enum": ["purchase-intent", "click-through-rate", "scroll-stopping",
                         "engagement", "trustworthiness", "conversion-potential"],
                "default": "purchase-intent"
            }
        },
        "required": ["assets", "audience"]
    }
}]

Security Best Practices

• Never expose your API key in client-side code. Use environment variables or a server-side proxy.
• Use scoped keys when possible. MCP keys have scopes like assets.read, assets.generate, and assets.edit. Create keys with only the scopes your agent needs.
• Monitor credit usage. Set up alerts for unusual consumption patterns. An agent bug that loops can burn through credits quickly.
• Rate limit your agent. Even though Kettio enforces rate limits (60 scoring evaluations/min for ranking, 10 images/min for generation), add your own limits to prevent runaway loops.

Frequently Asked Questions

What is MCP (Model Context Protocol)?

MCP is an open standard from Anthropic that lets AI agents discover and use tools through a standardized interface. Instead of writing custom API integration code, you install an MCP server and the agent can immediately use its tools. Think of it as USB for AI tools.

Do I need Claude Desktop to use Kettio with agents?

No. The MCP server is a convenience for Claude Desktop users. You can use Kettio's REST API directly with any agent framework — LangChain, CrewAI, AutoGPT, custom agents, or plain HTTP requests.

Can I use Kettio MCP with other LLMs?

MCP is currently best supported in Claude Desktop. However, MCP is an open standard and other clients are emerging. You can also use the REST API directly with any LLM that supports tool use.

How much does it cost to run an AI marketing agent with Kettio?

Kettio charges per successful scoring evaluation. Image-only assets usually use 1 credit; assets with copy context can use 2 because Kettio also runs an image-only ablation. A typical image-only workflow that scores 10 creatives against 3 audience segments uses 30 credits. Image generation costs vary by model. All accounts start with 50 free credits.

Can agents publish ads directly through Kettio?

Kettio integrates with Meta, Pinterest, and TikTok for ad publishing. Currently these integrations require an authenticated web session (OAuth). API-driven publishing is on the roadmap.