How to Use the Judge.me MCP in LangChain

Q: How do I connect Judge.me to LangChain?

Install langchain-mcp-adapters and use MultiServerMCPClient. Pass your connection details and call client.gettools() to feed the endpoints to your agent.

Q: Does LangChain support Judge.me media attachments?

Yes. Your agent calls listmedias to retrieve images and videos attached to customer feedback. You then pass those URLs to vision models within your chain.

Build multi-step reasoning pipelines that manage your e-commerce reviews using the Judge.me MCP Server for LangChain.

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Connect Judge.me MCP to LangChain

Create your Vinkius account to connect Judge.me 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 Judge.me with LangChain

Ask AI about this MCP

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Claude

Perplexity

Wire LangChain to your review data

`list_reviews` pulls your store's customer feedback directly into your ReAct agent's working memory. You define the logic, and the agent decides when to fetch recent ratings or pull specific customer complaints. If an agent spots a negative rating, it triggers `get_review` to grab metadata and moderation status. This output immediately becomes the input for your next chain, letting you build automated support workflows without writing custom scripts.

Audit Q&A pipelines automatically

Using `list_questions` exposes every pending customer inquiry from your shop directly to your chain. Your agent reads the raw text and status, then passes that data to a specialized LLM for draft generation. Once drafted, you use `get_question` to verify the original context before preparing an official answer. You track every MCP API call, latency, and token usage through LangSmith while your agents chew through the backlog.

Analyze product performance

The `list_products` endpoint returns your entire catalog alongside aggregate review counts, giving your agents a direct look at what sells. The agent sorts through this list to identify items that need more social proof. When it finds a product lacking reviews, it calls `get_product` for a detailed performance summary. Your chain then checks `list_coupons` to see if you have active discount incentives ready to send to past buyers.

Setup guide

Set up Judge.me 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 Judge.me 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({
    "judgeme-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 Judge.me 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 Judge.me. 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 Judge.me MCP in LangChain

Install `langchain-mcp-adapters` and use `MultiServerMCPClient`. Pass your connection details and call `client.get_tools()` to feed the endpoints to your agent.

The agent fetches the raw text and ratings directly from the API. You configure the chain to route low ratings to a support queue and high ratings to a marketing workflow.

Yes. Your agent calls `list_medias` to retrieve images and videos attached to customer feedback. You then pass those URLs to vision models within your chain.

Every MCP call logs automatically in LangSmith. You see the exact inputs sent to the endpoints and the raw JSON returned.

This server accesses customer names, review text, and email settings via `get_settings`. The connection runs through an isolated V8 sandbox, meaning your LangChain environment receives the exact requested data without exposing your core infrastructure to third-party scripts.

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