Refiner MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Check Refiner Status, Get Refiner Contact, Identify Refiner User, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Refiner as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Refiner app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Refiner. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Refiner?"
)
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 Refiner MCP Server
Connect your Refiner customer feedback account to any AI agent and simplify how you collect in-product insights, manage user segments, and monitor survey performance through natural conversation.
LlamaIndex agents combine Refiner tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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.
What you can do
- Survey Oversight — List all in-app, email, and link surveys and retrieve detailed status and response counts.
- Response Analysis — Query survey submissions with technical filters like UUIDs and date ranges to identify trends.
- Identity & Targeting — Identify users and upsert technical traits to ensure surveys reach the right audience.
- Event-Driven Feedback — Track high-fidelity user actions programmatically to trigger perfectly timed micro-surveys via AI.
- Segment Intelligence — List and query defined user segments to understand your audience distribution.
- Operational Monitoring — Check API health and verify account configurations directly from the agent.
The Refiner MCP Server exposes 8 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.
All 8 Refiner tools available for LlamaIndex
When LlamaIndex connects to Refiner through Vinkius, your AI agent gets direct access to every tool listed below — spanning customer-feedback, nps-surveys, user-insights, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Check API Status
Get contact details
Identify or update user
List product contacts
List survey responses
List user segments
List feedback surveys
Track user event
Connect Refiner to LlamaIndex via MCP
Follow these steps to wire Refiner into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Refiner MCP Server
LlamaIndex provides unique advantages when paired with Refiner through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Refiner tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Refiner tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Refiner, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Refiner tools were called, what data was returned, and how it influenced the final answer
Refiner + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Refiner MCP Server delivers measurable value.
Hybrid search: combine Refiner real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Refiner 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 Refiner for fresh data
Analytical workflows: chain Refiner queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Refiner in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Refiner immediately.
"List all my feedback surveys in Refiner."
"Show me the last 5 responses for the 'NPS - Post Checkout' survey."
"Track event 'Clicked Upgrade' for user 'mike@example.com'."
Troubleshooting Refiner MCP Server with LlamaIndex
Common issues when connecting Refiner to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRefiner + LlamaIndex FAQ
Common questions about integrating Refiner MCP Server with LlamaIndex.