How to Use the Veraset MCP in LangChain

Q: How can I monitor a long-running task using Veraset and LangChain?

After initiating a complex SQL job via executesqlquery, you don't need to wait. You check the progress status immediately by calling getquerystatus.

Q: What is the process for retrieving final query data with Veraset and LangChain?

The getqueryresults tool pulls the completed rows from a finished SQL job. It supports pagination, so you can handle massive result sets without hitting limits.

Execute complex data pipelines with LangChain using Veraset's mobility dataset tools.

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 LangChain

Create your Vinkius account to connect Veraset to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build Multi-Step Reasoning Pipelines with LangChain

Your agent uses `list_mobility_datasets` to identify which geospatial datasets exist. After picking a target, it calls `get_dataset_schema` to understand the columns and types before writing any query.

Managing Data Queries via MCP Server

Need to run an analytical task? You can start a new SQL job using `execute_sql_query`. This returns a unique query ID, which you then use with `get_query_status` to track progress until it finishes.

Handling Large Data Transfers in LangChain

Once the data is ready, your client can generate temporary download links. The `generate_download_link` tool creates a pre-signed URL for an S3 file, letting you pull the results directly into your chain's next step.

Setup guide

Set up Veraset MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Veraset tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "veraset-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Veraset transactions"
    })
    print(result["messages"][-1].content)

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 LangChain

Veraset primarily handles geospatial mobility datasets. The tools provide metadata and schemas, confirming the structure of your raw data before any query runs.

After initiating a complex SQL job via `execute_sql_query`, you don't need to wait. You check the progress status immediately by calling `get_query_status`.

Absolutely. Your agent can chain together tool calls: list datasets, get schema, run query, and download the results—all within one multi-step flow.

The `get_query_results` tool pulls the completed rows from a finished SQL job. It supports pagination, so you can handle massive result sets without hitting limits.

This MCP Server touches mobility datasets stored in S3 buckets. Always remember to manage access tokens tightly when designing your multi-step chain.

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