Userback MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Feedback Entry, Get Feedback Details, Get Project Details, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Userback 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 Userback app connector for LlamaIndex is a standout in the Developer Tools category — giving your AI agent 6 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 Userback. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Userback?"
)
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 Userback MCP Server
Connect your Userback account to any AI agent and simplify how you collect and manage visual feedback, bug reports, and user suggestions through natural conversation.
LlamaIndex agents combine Userback tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Feedback Management — List all feedback entries and retrieve detailed metadata, screenshots, and comments for specific reports.
- Project Control — List and query feedback projects to keep your development and design work organized.
- Direct Creation — Programmatically create new feedback entries or bug reports for specific projects via AI.
- Team Visibility — List account users and collaborators to understand your organization's review team.
- Status Tracking — Monitor the progress of feedback items and verify if issues have been resolved.
The Userback MCP Server exposes 6 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 6 Userback tools available for LlamaIndex
When LlamaIndex connects to Userback through Vinkius, your AI agent gets direct access to every tool listed below — spanning visual-feedback, bug-reporting, user-experience, 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.
Create a new feedback entry
Get details for a specific feedback
Get details for a specific project
List account users
List Userback feedbacks
List Userback projects
Connect Userback to LlamaIndex via MCP
Follow these steps to wire Userback 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 Userback MCP Server
LlamaIndex provides unique advantages when paired with Userback through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Userback tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Userback tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Userback, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Userback tools were called, what data was returned, and how it influenced the final answer
Userback + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Userback MCP Server delivers measurable value.
Hybrid search: combine Userback real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Userback 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 Userback for fresh data
Analytical workflows: chain Userback queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Userback in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Userback immediately.
"List all feedback projects in my Userback account."
"Show me the latest bug reports for the 'Product App v2' project."
"Create a new suggestion: 'Add dark mode support' to project '10293'."
Troubleshooting Userback MCP Server with LlamaIndex
Common issues when connecting Userback to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUserback + LlamaIndex FAQ
Common questions about integrating Userback MCP Server with LlamaIndex.