Veraset MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Veraset as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Veraset. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Veraset?"
)
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 Veraset MCP Server
Bind the massive scale of Veraset geolocation data directly to your preferred AI conversational agent. Eradicate context switching when analyzing billions of Points of Interest (POI) and mobile signal attributes.
LlamaIndex agents combine Veraset tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Live SQL Querying — Prompt your LLM agent to construct, dispatch, and execute ANSI SQL directly aimed at Veraset databases to compute geolocation aggregates.
- Rapid Execution Management — Check on long-running geolocation jobs, pull back the output tables seamlessly, or ruthlessly cancel intensive queries straight from your text box.
- Dataset Profiling — Scan all your available Veraset packages, request quick dataset schemas, or instantly preview data samples to ensure accuracy before executing queries.
- Delivery Bucket Access — Query the secure S3 delivery prefixes attached to your organization for bulk downloads and dynamically generate pre-signed file keys in seconds.
The Veraset MCP Server exposes 10 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.
How to Connect Veraset to LlamaIndex via MCP
Follow these steps to integrate the Veraset MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Veraset
Why Use LlamaIndex with the Veraset MCP Server
LlamaIndex provides unique advantages when paired with Veraset through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Veraset tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Veraset tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Veraset, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Veraset tools were called, what data was returned, and how it influenced the final answer
Veraset + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Veraset MCP Server delivers measurable value.
Hybrid search: combine Veraset real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Veraset 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 Veraset for fresh data
Analytical workflows: chain Veraset queries with LlamaIndex's data connectors to build multi-source analytical reports
Veraset MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Veraset to LlamaIndex via MCP:
cancel_running_query
Immediately aborts a currently executing SQL task
execute_sql_query
Provide a dataset ID and ANSI SQL. Returns a query ID. Starts a new SQL query task against a Veraset dataset
generate_download_link
Generates a temporary pre-signed URL for an S3 file download
get_dataset_metadata
Retrieves technical metadata for a specific mobility dataset
get_dataset_sample
Retrieves a quick sample of the first few rows of a dataset
get_dataset_schema
Retrieves the column definitions and data types for a dataset
get_query_results
Supports pagination. Retrieves the result rows from a completed SQL query
get_query_status
Checks the progress of a running SQL query
list_mobility_datasets
Identify accessible mobility datasets in Veraset
list_s3_delivery_folders
Lists S3 prefixes where scheduled data drops are delivered
Example Prompts for Veraset in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Veraset immediately.
"List all our provisioned delivery folder buckets for S3 mobility packets."
"Get a basic preview 10-row sample from the dataset 'movement_global'."
"Execute an aggregation query on 'dataset-v5' grouping total foot traffic by 'store_id' and get the current execution status."
Troubleshooting Veraset MCP Server with LlamaIndex
Common issues when connecting Veraset to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVeraset + LlamaIndex FAQ
Common questions about integrating Veraset MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Veraset with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Veraset to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.