How to Use the GatherUp MCP in LangChain

Q: Does GatherUp allow my LangChain agent to reply to reviews?

It does. Your agent uses replytoonlinereview to push responses, assuming you have the proper authentication tokens configured in your environment.

Chain your review management workflows in LangChain by connecting the GatherUp MCP server for automated feedback loops.

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Connect GatherUp MCP to LangChain

Create your Vinkius account to connect GatherUp 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 GatherUp with LangChain

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Claude

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Automate review outreach within LangChain

Trigger `send_review_invite` directly from your agentic pipelines. You define the logic for when an invite goes out, keeping the timing tight to the customer interaction. Your agent monitors the process by checking `get_review_metrics` after each sequence. If the metrics stall, the chain adjusts its parameters automatically.

Sync customer data across your MCP chain

Pull contact records using `list_customer_contacts` to feed into your downstream reasoning tasks. This gives your agent the context needed to personalize every interaction without manual intervention. Use `add_new_customer` to update your database when a new contact enters the system. It keeps your records fresh as the agent processes incoming data streams.

Analyze feedback loops with LangSmith

Trace every `list_online_reviews` call through your LangSmith dashboard to debug why an agent picked a specific response. You get full visibility into the input data and the resulting tool output. Run `search_all_reviews` to find patterns in negative sentiment. Your agent uses these findings to refine its next set of actions in the chain.

Setup guide

Set up GatherUp 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 GatherUp 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({
    "gatherup-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 GatherUp transactions"
    })
    print(result["messages"][-1].content)

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Common questions about GatherUp MCP in LangChain

Install the necessary MCP adapters and point your client to the Vinkius endpoint. You then fetch the tool list and inject them into your agent definition.

Yes, but you need to manage the rate limits yourself. Since LangChain is stateless, ensure your logic accounts for API throttling when triggering `send_review_invite`.

It does. Your agent uses `reply_to_online_review` to push responses, assuming you have the proper authentication tokens configured in your environment.

Your customer contact and review data stays within your controlled environment. We use ephemeral, zero-trust connections between your agent and the server.

The MCP server reports the error back to the agent. You should implement standard try-catch blocks in your chain to handle these specific failure states.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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