How to Use the Userback MCP in LlamaIndex

Q: Can I query past Userback data using LlamaIndex?

Yes. You combine the output of tools like getprojectdetails and listfeedbacks and index them together. This lets you ask questions about historical context that simple API calls can't answer.

Q: Does Userback support data access filtering with LlamaIndex?

The client supports an allowedtools filter, letting you specify exactly which resources from the MCP Server are included in your index. This keeps your knowledge base tight and focused on what matters.

Index Userback results into vector stores with LlamaIndex.

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Connect Userback MCP to LlamaIndex

Create your Vinkius account to connect Userback 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 Userback with LlamaIndex

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Search past projects using the MCP Server.

Start by calling `list_userback_projects` to get a list of all available projects. You then feed these project names into your RAG application, indexing them so they become part of your searchable knowledge base. Later, you can query past sessions or configurations and get answers grounded in actual API data—not just hallucinations.

Build a feedback index with LlamaIndex.

The `list_feedbacks` tool provides the raw material for your knowledge base. By indexing this output, you create semantic search capabilities over all recorded user feedback. Instead of just listing results, you can ask complex questions like 'What were the most common bugs reported on Project X?' and get an answer citing specific records.

Track users via MCP Server.

Use `list_account_users` to pull current user data. This output is essential for grounding your knowledge base, allowing you to connect feedback entries directly to the people who submitted them. This combined approach means your AI client can answer questions based on both textual descriptions and structured API data.

Setup guide

Set up Userback 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 Userback 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 Userback tools.",
)
response = await agent.run("List recent Userback 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 Userback. 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 Userback MCP in LlamaIndex

LlamaIndex doesn't just call the tools; it indexes the results. When you run `list_feedbacks`, the output becomes searchable knowledge, allowing your agent to retrieve highly specific answers from past records.

The Userback MCP Server manages project metadata, user lists, and detailed feedback entries. These structured outputs are ideal for combining with documents into a unified index.

Yes. You combine the output of tools like `get_project_details` and `list_feedbacks` and index them together. This lets you ask questions about historical context that simple API calls can't answer.

The client supports an allowed_tools filter, letting you specify exactly which resources from the MCP Server are included in your index. This keeps your knowledge base tight and focused on what matters.

Since this server touches user account information, ensure that any sensitive details pulled via `list_account_users` are appropriately masked or restricted before they become part of your searchable knowledge index.

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