Judge.me MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Judge.me through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"judgeme": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Judge.me, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Judge.me MCP Server
Empower your AI agents with Judge.me's comprehensive product review platform. This MCP server allows you to list and retrieve product reviews, track customer questions and answers, manage coupons, and view shop settings directly through the Judge.me API. Ideal for automating social proof management and customer feedback analysis.
LangChain's ecosystem of 500+ components combines seamlessly with Judge.me through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
The Judge.me MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Judge.me to LangChain via MCP
Follow these steps to integrate the Judge.me MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Judge.me via MCP
Why Use LangChain with the Judge.me MCP Server
LangChain provides unique advantages when paired with Judge.me through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Judge.me MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Judge.me queries for multi-turn workflows
Judge.me + LangChain Use Cases
Practical scenarios where LangChain combined with the Judge.me MCP Server delivers measurable value.
RAG with live data: combine Judge.me tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Judge.me, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Judge.me tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Judge.me tool call, measure latency, and optimize your agent's performance
Judge.me MCP Tools for LangChain (10)
These 10 tools become available when you connect Judge.me to LangChain via MCP:
get_product
Essential for providing a summary of a product's performance. Retrieves details for a specific product
get_question
Use this before preparing an official answer. Retrieves details for a specific question
get_review
Returns deep-dive information including metadata and moderation status. Use this when analyzing a particular customer case or response. Retrieves details for a specific review
get_settings
me app for the shop, including review widgets and email settings. Useful for system configuration auditing. Retrieves shop settings for Judge.me
list_answers
Use this to audit responses and check if queries have been resolved. Lists all answers to questions
list_coupons
me (often used as rewards for reviews). Useful for auditing incentive programs. Lists all active discount coupons
list_medias
Essential for analyzing user-generated content (UGC). Lists all media (images/videos) attached to reviews
list_products
me. Includes product names, IDs, and aggregate review counts. Use this to identify products for review analysis. Lists all products in the shop
list_questions
me Q&A feature. Returns question text, status, and IDs. Use this to identify customer inquiries that need answers. Lists all customer questions
list_reviews
me platform. Returns reviewer names, ratings, review titles, and bodies. Use this to monitor customer sentiment and analyze product feedback. Lists all product reviews
Example Prompts for Judge.me in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Judge.me immediately.
"List all recent product reviews in Judge.me."
"Show me the questions asked for product ID '123'."
"Check for any active discount coupons."
Troubleshooting Judge.me MCP Server with LangChain
Common issues when connecting Judge.me to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersJudge.me + LangChain FAQ
Common questions about integrating Judge.me MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Judge.me with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Judge.me to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.