How to Use the Judge.me MCP in LlamaIndex

Q: How do I configure Judge.me with LlamaIndex?

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Wrap it in McpToolSpec and pass the MCP integration to your FunctionAgent.

Q: Can LlamaIndex query Judge.me product questions?

Yes. Your agent uses listquestions and listanswers to build an index of common customer inquiries. This turns past support tickets into a searchable knowledge base.

Q: Does LlamaIndex track active Judge.me coupons?

Your agent calls listcoupons to retrieve active discount codes. You then embed these into your store's automated chat responses.

Index your e-commerce reviews and product Q&A into searchable vector stores with the Judge.me MCP server for LlamaIndex.

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Connect Judge.me MCP to LlamaIndex

Create your Vinkius account to connect Judge.me to LlamaIndex 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 LlamaIndex

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Index UGC with this MCP Server

Calling `list_reviews` feeds your raw customer feedback straight into your LlamaIndex documents. Instead of just reading text, your RAG application embeds reviewer names, ratings, and body text into a unified knowledge base. When a user queries your system about product defects, the engine searches the vector store rather than hallucinating. If it needs deeper context on a specific complaint, it fires `get_review` to pull the moderation status and metadata.

Ground answers in real product data

`list_products` injects your current catalog and aggregate review counts into your semantic search pipeline. Your AI client knows exactly which items carry the highest ratings because it queries the actual shop data. For granular queries, the agent triggers `get_product` to fetch the performance summary of a specific item. This ensures your sales bots or internal dashboards always cite accurate review volumes.

Search visual feedback and settings

Your integration uses `list_medias` to grab every user-uploaded image and video attached to your reviews. You index these URLs alongside the text, building a multimodal RAG application. Your system also indexes your shop configuration by calling `get_settings`. If someone asks how the review widgets are configured, the agent retrieves the exact email and display parameters currently running in production.

Setup guide

Set up Judge.me MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Judge.me MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Judge.me tools.",
)
response = await agent.run("List recent Judge.me data")

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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 LlamaIndex

Install `llama-index-tools-mcp` and instantiate a `BasicMCPClient`. Wrap it in `McpToolSpec` and pass the MCP integration to your `FunctionAgent`.

Yes. Your agent uses `list_questions` and `list_answers` to build an index of common customer inquiries. This turns past support tickets into a searchable knowledge base.

Raw MCP tool calls lack semantic memory. Indexing them means your RAG application answers complex questions about overall customer sentiment without hitting rate limits.

Your agent calls `list_coupons` to retrieve active discount codes. You then embed these into your store's automated chat responses.

The integration reads sensitive UGC, including reviewer names, raw question text, and media attachments. Vinkius enforces a zero-trust architecture, so your API tokens remain securely managed at the endpoint layer while your LlamaIndex app only ingests the resulting JSON from this MCP Server.

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