How to Use the Birdeye MCP in LangChain

Q: How do I pass Birdeye tools to a LangChain agent?

You use the langchain-mcp-adapters package to convert the MCP tools. Call client.gettools() on your Vinkius client and pass the resulting list directly into your agent's constructor to expose actions like listcontacts and replytoreview.

Feed Birdeye review data directly into your LangChain reasoning loops and watch your agents trigger customer surveys on the fly.

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Connect Birdeye MCP to LangChain

Create your Vinkius account to connect Birdeye 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 Birdeye with LangChain

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Trigger Birdeye customer check-ins inside LangChain chains

The `checkin_customer` tool plugs directly into your LangChain ReAct agents to run customer-support workflows without manual intervention. When a support ticket closes, your agent uses this tool to trigger a review request immediately. You trace every `checkin_customer` call with LangSmith. If the Birdeye check-in fails, your LangChain agent catches the error and adjusts its next step.

Feed live Birdeye reviews to LangChain reasoning agents

The `list_reviews` tool fetches raw Birdeye reviews directly into your LangChain context window. Your LangChain agent reads the text, extracts the sentiment, and decides whether to write a response or escalate the issue. Once your LangChain agent drafts a response, it calls `reply_to_review` to post it back directly. This lets you build closed-loop sentiment analysis pipelines where the agent handles the entire feedback cycle.

Run multi-step Birdeye survey analysis with an MCP Server

The `list_surveys` tool lets your LangChain agent pull active Birdeye customer campaigns directly into your pipeline. Your chain queries the list, identifies the correct campaign ID, and then invokes `get_survey_responses` to pull down the raw answers. Because this runs on a standard MCP Server, you combine these Birdeye survey results with other tools in your LangChain graph. Your LangChain agent parses the Birdeye data to spot trends and update your database in one run.

Setup guide

Set up Birdeye 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 Birdeye 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({
    "birdeye-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 Birdeye 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 Birdeye. 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 Birdeye MCP in LangChain

You use the `langchain-mcp-adapters` package to convert the MCP tools. Call `client.get_tools()` on your Vinkius client and pass the resulting list directly into your agent's constructor to expose actions like `list_contacts` and `reply_to_review`.

Yes, LangChain excels at sequential tool execution. An agent can call `get_review_summary` to spot low ratings, retrieve the specific customer details with `get_contact`, and then trigger a survey using `checkin_customer` in a single execution loop.

Use LangSmith to track every tool execution. You will see the exact inputs sent to `reply_to_review` and the raw payloads returned from `get_survey_responses`, making debugging straightforward.

No, Vinkius handles the authentication layer for you. Your LangChain code only needs the single endpoint token to connect to the hosted server and run tools like `list_locations` safely.

All customer reviews and contact details processed by this server run inside secure, ephemeral V8 isolates. Vinkius never stores your customer data or API payloads, ensuring that sensitive feedback remains private.

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