How to Use the Cabify MCP in AutoGen

Q: How do AutoGen agents negotiate Cabify ride bookings?

One agent pulls rates using getpriceestimate while another checks corporate budget limits. They discuss the options in a conversation loop before calling requestride.

Q: Can I use the Cabify MCP Server to manage travel budgets in AutoGen?

Yes. Your agents use getridehistory to pull past trip costs and analyze corporate travel spend. A budget agent can then restrict future ride requests based on these historical metrics.

Q: How do I register Cabify tools with my AutoGen agents?

Install with pip install -U autogen-extmcp. Use mcpservertools with StreamableHttpServerParams to fetch the tools, then pass them directly to your AssistantAgent constructor.

Q: What happens if a Cabify ride needs to be cancelled?

An agent can invoke cancelride if the conversation logic decides a trip is no longer necessary. The tool returns the updated status to the agent group.

Let AutoGen agents debate and coordinate Cabify ride bookings and corporate travel expenses.

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Setup Cabify with AutoGen

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Multi-agent Cabify booking negotiation in AutoGen

The Cabify MCP Server enables your AutoGen agents to coordinate rides using `request_ride` and `cancel_ride`. An AutoGen booking agent proposes a trip, while an AutoGen finance agent reviews the cost before the Cabify ride is dispatched. If the corporate budget is exceeded, the AutoGen finance agent triggers `cancel_ride` or requests a lower tier. The AutoGen agents debate the Cabify travel options autonomously until they reach a consensus that fits your business guidelines.

Debate Cabify service tiers with AutoGen

This MCP Server exposes `get_available_products` and `get_price_estimate` to your AutoGen conversations. Your AutoGen performance agent requests the fastest route using `get_time_estimate`, while the AutoGen budget agent checks the price. The AutoGen agents analyze the live Cabify data side-by-side. They negotiate whether a premium Cabify service tier is justified for a specific client meeting, resolving the conflict before executing the ride request.

Audit Cabify travel history using AutoGen teams

The Cabify MCP Server lets an AutoGen audit team query ride metrics using `get_ride_history` and `get_saved_locations`. An AutoGen analyst agent pulls the history, and a compliance agent matches it against your saved Cabify office locations. This multi-agent review flags Cabify rides that started or ended outside approved corporate zones. The entire conversation and audit trail happen autonomously within your configured AutoGen agent group.

Setup guide

Set up Cabify MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Cabify tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Cabify_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cabify data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Cabify_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Cabify data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cabify. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cabify MCP in AutoGen

One agent pulls rates using `get_price_estimate` while another checks corporate budget limits. They discuss the options in a conversation loop before calling `request_ride`.

Yes. Your agents use `get_ride_history` to pull past trip costs and analyze corporate travel spend. A budget agent can then restrict future ride requests based on these historical metrics.

Install with pip install -U autogen-ext[mcp]. Use mcp_server_tools with StreamableHttpServerParams to fetch the tools, then pass them directly to your AssistantAgent constructor.

An agent can invoke `cancel_ride` if the conversation logic decides a trip is no longer necessary. The tool returns the updated status to the agent group.

Vinkius processes all API requests in a zero-trust, ephemeral sandbox. Your corporate billing details and expense categories are never cached or exposed to other agents in the network.

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