How to Use the Cabify MCP in LangChain

Q: How do I pass Cabify location IDs into a LangChain agent?

Use getsavedlocations to retrieve the active IDs. Your LangChain agent then parses this list and inserts the selected location ID directly into the requestride tool call.

Q: Can I monitor Cabify ride costs using LangChain and LangSmith?

Yes. Every time your chain calls getpriceestimate or getridehistory, LangSmith records the exact payload and cost metrics. You track these tool outputs in the LangSmith dashboard to audit corporate travel spend.

Q: How does the Cabify MCP Server handle ride cancellations in LangChain?

Your agent invokes cancelride when a state change triggers a cancel event in LangGraph. The tool returns the cancellation status, which the agent uses to update your application state.

Chain Cabify ride bookings and fare estimates directly into your LangChain LLM pipelines.

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Connect Cabify MCP to LangChain

Create your Vinkius account to connect Cabify to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cabify with LangChain

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Automate Cabify dispatch inside LangChain pipelines

The Cabify MCP Server exposes `request_ride` and `cancel_ride` directly to your LangChain agents as executable tools. Your LangChain chain takes a raw user prompt, extracts coordinates, and books the Cabify vehicle without manual dispatch. If a Cabify ride needs to be cancelled, the agent executes `cancel_ride` based on logic defined in your LangGraph state machine. You monitor these multi-step booking decisions in real time using LangSmith tracing to verify exact Cabify tool inputs.

Multi-step Cabify fare and route analysis

This MCP Server connects `get_price_estimate` and `get_time_estimate` to your LangChain sequential chains. Your LangChain agent first checks available options with `get_available_products`, then feeds those product IDs directly into the Cabify price estimator. By passing outputs from one Cabify tool as inputs to the next, your LangChain agent calculates the most cost-effective ride tier before initiating the booking. This chain-based execution prevents your LangChain system from requesting Cabify rides blindly.

Track corporate travel history in LangChain

The Cabify MCP Server lets your LangChain agent query your corporate travel records using `get_ride_history` and `get_saved_locations`. The agent pulls past Cabify trip costs and coordinates, feeding them into your custom LangChain reporting chains. You configure these Cabify tools within a MultiServerMCPClient instance alongside your database tools. This setup lets your LangChain agent compare Cabify expenses against your internal budget databases in a single execution loop.

Setup guide

Set up Cabify MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cabify tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cabify-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cabify transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Use `get_saved_locations` to retrieve the active IDs. Your LangChain agent then parses this list and inserts the selected location ID directly into the `request_ride` tool call.

Yes. Every time your chain calls `get_price_estimate` or `get_ride_history`, LangSmith records the exact payload and cost metrics. You track these tool outputs in the LangSmith dashboard to audit corporate travel spend.

Your agent invokes `cancel_ride` when a state change triggers a cancel event in LangGraph. The tool returns the cancellation status, which the agent uses to update your application state.

Run pip install langchain-mcp-adapters langgraph. Initialize MultiServerMCPClient pointing to the Vinkius URL, call client.get_tools(), and pass them to your agent constructor.

Vinkius runs the server in an isolated V8 sandbox, preventing access to your corporate ride history data. Your API tokens and coordinate history remain encrypted and are never exposed to the LLM provider.

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