Judge.me MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Judge.me as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Judge.me. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Judge.me?"
)
print(response)
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.
LlamaIndex agents combine Judge.me tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
The Judge.me MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Judge.me MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Judge.me
Why Use LlamaIndex with the Judge.me MCP Server
LlamaIndex provides unique advantages when paired with Judge.me through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Judge.me tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Judge.me tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Judge.me, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Judge.me tools were called, what data was returned, and how it influenced the final answer
Judge.me + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Judge.me MCP Server delivers measurable value.
Hybrid search: combine Judge.me real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Judge.me to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Judge.me for fresh data
Analytical workflows: chain Judge.me queries with LlamaIndex's data connectors to build multi-source analytical reports
Judge.me MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Judge.me to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Judge.me to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJudge.me + LlamaIndex FAQ
Common questions about integrating Judge.me MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.