How to Use the Veraset MCP in LlamaIndex

Q: How does LlamaIndex help with Veraset's complex data schemas?

LlamaIndex indexes the schema definitions pulled by getdatasetschema. This means your agent can query, 'Which column identifies a trip start time?' and get a precise answer.

Q: Does Veraset support continuous data access with LlamaIndex?

It does. By combining getdatasetsample output with the indexing process, you feed live API data into a unified knowledge base for real-time querying.

Q: What's best practice for managing queries when using Veraset and LlamaIndex?

Always run getquerystatus after starting an SQL job. The resulting status updates are indexed, helping your agent understand if the data is ready or still running.

Index Veraset's raw data and query insights with LlamaIndex via the MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Veraset MCP to LlamaIndex

Create your Vinkius account to connect Veraset 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.

GDPR Free for Subscribers

Setup Veraset with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Searchable Knowledge Base using LlamaIndex

LlamaIndex lets you index more than just text. When you run a dataset metadata check using `get_dataset_metadata`, that technical information becomes part of your searchable knowledge graph.

Advanced Data Retrieval with MCP Server

Need to know what's available? Run `list_mobility_datasets` first. Then, use the results to build a query using `execute_sql_query`. The tool output gets indexed, giving semantic context to your findings.

Building RAG Apps with LlamaIndex

The system can retrieve technical definitions via `get_dataset_schema` and immediately index those column names and types. You'll get answers grounded in the actual data structure, not just general knowledge.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Veraset. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Veraset now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Veraset MCP in LlamaIndex

LlamaIndex indexes the schema definitions pulled by `get_dataset_schema`. This means your agent can query, 'Which column identifies a trip start time?' and get a precise answer.

Yes. The MCP tool output—like the list of available folders from `list_s3_delivery_folders`—is indexed. You can query past session results and configurations.

It does. By combining `get_dataset_sample` output with the indexing process, you feed live API data into a unified knowledge base for real-time querying.

Always run `get_query_status` after starting an SQL job. The resulting status updates are indexed, helping your agent understand if the data is ready or still running.

This server touches mobility datasets in S3 folders. You must control access to the folder names and dataset IDs that are indexed into your vector store.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript