# MCP Integration

Every Cafecito API product ships an **MCP (Model Context Protocol) server** — same auth, same key, zero extra setup. Point your AI agent or MCP client at the endpoint and go.

## Authentication
<Button className="btn-with-link" asChild>
  <a href="/settings/api-keys">Get API Key</a>
</Button>
:::tip
Your existing API key works for MCP calls — they share the same rate limit and quota meter as regular API calls.
:::

## Connecting to MCP Servers
**Server URL**: `https://api.cafecito.tech/<product>/mcp` 

```
Authorization: Bearer YOUR-API-KEY
```

## Beans MCP server
**Server URL**: `https://api.cafecito.tech/beans/mcp`

```python title="Using Beans MCP with your Agent"
# openai-agents.HostedMCPTool (https://github.com/openai/openai-agents-python)
import os
import asyncio
from agents import Agent, HostedMCPTool, Runner

mcp_tool_config = {
    "type": "mcp",
    "server_label": "cafecito_beans",
    "server_url": "https://api.cafecito.tech/beans/mcp",
    "authorization": os.getenv("CAFECITO_API_KEY"),    
    "require_approval": "never",
}

# SETUP: model client using AsyncOpenAI
# SETUP: model using OpenAIChatCompletionsModel

agent = Agent(
    name="News Analyst",
    instructions="EXTRACT news headlines and associated briefings",
    tools=[
        HostedMCPTool(tool_config=mcp_tool_config)
    ],
)

PROMPT_TEMPLATE = """
Today: {date}
Question: What is trending on middle east today?
"""
  
result = Runner.run_sync(agent, PROMPT_TEMPLATE.format(date=datetime.now().strftime("%Y-%m-%d")))
print("=== Final output ===")
print(result.final_output)
```

## Use cases

- AI assistants and RAG workflows that need fresh news context
- AI agents that monitor trends, entities, or topics in real time
- LLM pipelines that require enrichment-ready JSON, not raw HTML

## Related
- [Cafecito](https://cafecito.tech)
- [Pricing](/pricing)
- [Contact](/contact)