Guardian Engine — API & MCP Integration Guide

LLMs generate, Guardian verifies. Deterministic recipe verification for AI agents — catches hallucinated temperatures, missing techniques, wrong ingredients, and impossible cooking steps before they reach the pan.

Endpoint: https://api.kaimeilabs.dev/mcp
Transport: Streamable HTTP (MCP)
Auth: None — free during early access (fair use applies)

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Connect Your Agent

Guardian is a hosted MCP server. No install, no API key, no Docker. Pick your client and paste the config.

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "guardian": {
      "url": "https://api.kaimeilabs.dev/mcp",
      "transport": "streamable-http"
    }
  }
}

Restart Claude Desktop. Ask: "List the available dishes in Guardian Engine" to confirm.

Cursor

Open Settings → MCP Servers → Add new MCP server, then paste:

{
  "guardian": {
    "url": "https://api.kaimeilabs.dev/mcp",
    "transport": "streamable-http"
  }
}

VS Code (GitHub Copilot)

Add to your .vscode/mcp.json (or user settings.json under "mcp"):

{
  "servers": {
    "guardian": {
      "type": "http",
      "url": "https://api.kaimeilabs.dev/mcp"
    }
  }
}

Windsurf

Add to your Windsurf MCP config:

{
  "mcpServers": {
    "guardian": {
      "serverUrl": "https://api.kaimeilabs.dev/mcp"
    }
  }
}

Smithery (One-Click)

— auto-configures Claude Desktop, Cursor, and more.

(Note to Smithery users: The default Smithery proxy URL guardian-engine--kaimeilabs.run.tools does not support Streaming HTTP. Use https://api.kaimeilabs.dev/mcp directly.)

Glama.ai

Guardian Engine is also listed on Glama.ai — discover and connect to MCP servers from the Glama directory.

Any MCP Client (Python SDK)

import asyncio
from mcp.client.session import ClientSession
from mcp.client.streamable_http import streamable_http_client
from httpx import AsyncClient

async def main():
    async with AsyncClient(timeout=30.0) as http:
        async with streamable_http_client("https://api.kaimeilabs.dev/mcp", http_client=http) as streams:
            read_stream, write_stream, _ = streams
            async with ClientSession(read_stream, write_stream) as session:
                await session.initialize()
                result = await session.call_tool("list_dishes", arguments={"cuisine_filter": "french"})
                print(result)

asyncio.run(main())

pip install mcp>=1.2.1 httpx>=0.27.0

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Tools

verify_recipe

Verify a candidate recipe against a Guardian master recipe. Returns a structured report with verdict, score, and detailed findings.

Parameter	Type	Required	Description
dish	string	Yes	Name or alias of the dish (e.g. "carbonara", "rendang", "kung-pao", "bourguignon")
candidate_json	string	Yes	Full recipe as a JSON string — see schema.md
original_prompt	string	No	The user's original request that generated the recipe

Tip — pass the prompt for better feedback: When you include original_prompt (e.g. "Make a spicy vegan rendang"), Guardian activates Guided Oracle Mode: it reads the user's intent and returns specific, actionable improvement hints tailored to their request. Without it, Guardian returns only a Pass/Fail verdict and score.

list_dishes

List all master recipes Guardian can verify against.

Parameter	Type	Required	Description
cuisine_filter	string	No	Filter by cuisine (e.g. "french", "chinese", "thai")

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Available Recipes (20 dishes, 12 cuisines)

Cuisine	Dishes
French	Confit de Canard · Cheese Soufflé · Crème Brûlée · French Onion Soup · Coq au Vin · Beef Bourguignon
Chinese	Kung Pao Chicken · Cantonese Steamed Fish
Thai	Thai Green Curry
Indian	Chicken Tikka Masala
Indonesian	Beef Rendang
British	Beef Wellington
Italian	Pasta Carbonara · Risotto alla Milanese
Spanish	Basque Cheesecake
American	Southern Fried Chicken · Texas Smoked Brisket
Peruvian	Ceviche
European	Florentine Biscuits
Universal	Roast Chicken

All recipes accept multiple aliases (e.g. "gong-bao", "tikka", "risotto", "bourguignon"). Use list_dishes for the full live catalog — new dishes are added regularly.

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Files in This Repository

File	Purpose
schema.md	Complete candidate_json structure required by verify_recipe
client.py	Python example: submit a recipe for verification
test_integration.py	Live connectivity test against the public API
smithery.yaml	Smithery MCP registry configuration
glama.json	Glama.ai MCP server claim configuration

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Data & Privacy

• No PII collected — we do not store user names, emails, or API keys. Underlying cloud infrastructure may temporarily process IP addresses for routing.
• Data for Compute Exchange — the free service is provided in exchange for usage data. Submitted recipes are used to improve verification accuracy and create anonymized derived datasets. See our Terms of Service.
• Do not include PII in recipe payloads.
• Fair use quotas enforced via compute limits.

⚠️ Disclaimer: Verification results, including safety-related findings such as cooking temperatures and allergen warnings, are automated and informational only. They should not be relied upon as professional food safety, health, or culinary advice.

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Support & Contact

Building an AI cooking assistant, smart kitchen platform, or agentic food-tech product? We'd love to hear from you.

• Email: partners@kaimeilabs.dev
• Website: kaimeilabs.dev
• GitHub: github.com/kaimeilabs

License

Client code in this repository (client.py, test_integration.py) is released under the MIT License. The Guardian Engine verification logic and master recipe datasets are proprietary.