How to Use the Refiner MCP in LlamaIndex

Q: What tools should I exclude from LlamaIndex?

Use the allowedtools filter to limit access. Only expose the list and get tools if you want to prevent the agent from writing data.

Index your SaaS feedback into LlamaIndex using the Refiner MCP Server.

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Connect Refiner MCP to LlamaIndex

Create your Vinkius account to connect Refiner 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 Refiner with LlamaIndex

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Build RAG apps with Refiner data

Use `list_refiner_responses` to feed raw feedback into your vector store. LlamaIndex then indexes this data for semantic search. Query your knowledge base to find patterns in feature requests. The agent answers questions grounded in actual survey results rather than guessing.

Query user segments using LlamaIndex

Call `list_refiner_segments` to pull current user groupings into your index. Your RAG app gains awareness of who the user is before generating a response. Retrieve data via `get_refiner_contact` to provide specific answers to account-level questions. It links the feedback to the right entity.

Monitor survey health in your index

Run `list_refiner_surveys` to catalog existing feedback loops. LlamaIndex stores this metadata so your agent knows which surveys are active. Verify connection health with `check_refiner_status` to keep your index updated. If the API is down, your application knows to wait before trying to ingest new feedback.

Setup guide

Set up Refiner 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 Refiner 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 Refiner tools.",
)
response = await agent.run("List recent Refiner data")

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Common questions about Refiner MCP in LlamaIndex

You use the McpToolSpec to convert tools into a format LlamaIndex understands. This turns Refiner functions into searchable index entries.

Absolutely. Once the tool output is indexed, you can run natural language queries against your historical survey feedback.

You control the sync frequency by calling the tools as part of your ingestion pipeline. The data is as fresh as your last tool execution.

Use the allowed_tools filter to limit access. Only expose the list and get tools if you want to prevent the agent from writing data.

We keep your survey responses and user contacts in a sandboxed environment. Your index only holds the data you choose to pull through the authenticated MCP connection.

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