How to Use the DISQO MCP in LlamaIndex

Q: How does LlamaIndex handle semantic search over DISQO insights?

Run searchinsightsbykeyword to pull relevant reports, then let LlamaIndex index the raw text. This lets you run semantic queries over the results, finding deep behavioral patterns that keyword search alone might miss.

Q: Can I cache DISQO behavioral metrics in a LlamaIndex vector store?

Fetch data using listbehavioralmetrics and store the resulting definitions in your local index. This reduces API calls by letting LlamaIndex resolve schema questions locally before hitting the live server.

Q: How do I configure the DISQO MCP Server as a tool spec in LlamaIndex?

Install llama-index-tools-mcp and initialize the basic client with your Vinkius URL. Convert the client to a tool spec, and pass the resulting tool list directly to your LlamaIndex FunctionAgent.

Q: How secure are my DISQO project settings when indexed by LlamaIndex?

The MCP server retrieves project settings via getprojectdetails over an encrypted Vinkius connection, meaning your API tokens are never exposed. LlamaIndex stores this data in your local or private vector database, keeping your proprietary research parameters fully under your control.

Index DISQO consumer insights and behavioral reports into LlamaIndex vector stores to ground your queries in verified panel data.

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Connect DISQO MCP to LlamaIndex

Create your Vinkius account to connect DISQO 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 DISQO with LlamaIndex

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Index active DISQO research projects

`list_insight_projects` retrieves your current research portfolio so LlamaIndex can index the metadata directly into a vector store. This turns your active project list into a queryable knowledge base that grounds future agent responses. By avoiding hallucinations, your RAG system answers questions about active studies using actual project parameters. The agent queries this index first before deciding if it needs to make fresh API calls for live status updates.

Build RAG pipelines over DISQO consumer insights

`list_consumer_insights` pulls raw behavioral reports directly into LlamaIndex document structures. The framework chunks and indexes these reports, allowing your agent to perform semantic search over historical consumer data. This approach connects live market research with your local documentation. When a user asks about consumer trends, the system retrieves the most relevant chunks from the indexed reports to synthesize an accurate response.

Ground agent responses in DISQO MCP Server data

`list_largest_research_panels` identifies high-density audience segments, which LlamaIndex then stores as semantic nodes. Your query engine can compare these live panel sizes against historical target demographics stored in your vector database. Instead of guessing which audience to target, the agent uses real-time panel volumes to make recommendations. The entire process runs through the LlamaIndex query pipeline, ensuring every decision is backed by verified data.

Setup guide

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

Run `search_insights_by_keyword` to pull relevant reports, then let LlamaIndex index the raw text. This lets you run semantic queries over the results, finding deep behavioral patterns that keyword search alone might miss.

Fetch data using `list_behavioral_metrics` and store the resulting definitions in your local index. This reduces API calls by letting LlamaIndex resolve schema questions locally before hitting the live server.

Install `llama-index-tools-mcp` and initialize the basic client with your Vinkius URL. Convert the client to a tool spec, and pass the resulting tool list directly to your LlamaIndex FunctionAgent.

The tool spec handles pagination and converts the JSON payload into clean text documents. LlamaIndex then chunks this data automatically, preventing your LLM context window from overflowing during deep data retrievals.

The MCP server retrieves project settings via `get_project_details` over an encrypted Vinkius connection, meaning your API tokens are never exposed. LlamaIndex stores this data in your local or private vector database, keeping your proprietary research parameters fully under your control.

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