How to Use the Veraset MCP in Pydantic AI

Q: How do I check if my Veraset query is complete using Pydantic AI?

Use getquerystatus. This tool checks the current progress of your SQL task and tells you when it's finished, so your agent can proceed to gather results.

Q: Can my agent list all available datasets using Pydantic AI?

Yep. Call listmobilitydatasets. This gives your agent a comprehensive inventory of every accessible dataset ID Veraset has for mobility data.

Q: If I need to download results from a query using Pydantic AI, what's the process?

First, get the results with getqueryresults. If you need it as a file, use generatedownloadlink to create a temporary S3 URL. It’s a two-step process.

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Setup Veraset with Pydantic AI

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Execute and Manage SQL Queries

The `execute_sql_query` tool starts a new SQL task using a dataset ID and ANSI SQL, returning a query ID. You can confirm its status with `get_query_status`. Once finished, the results are pulled through `get_query_results`, which supports pagination. If you need to halt an operation mid-flight, use `cancel_running_query` to immediately abort any currently executing SQL task. This ensures your agent never works with stale or incomplete data.

Inspect Mobility Datasets

Before querying anything, list available datasets using `list_mobility_datasets`. When you pick one, check its column definitions and types with `get_dataset_schema`. For a quick gut-check on the data, run `get_dataset_sample`. For deeper understanding, `get_dataset_metadata` retrieves full technical metadata. You can also list where scheduled drops land using `list_s3_delivery_folders`.

Handle Data Downloads and Output

When the data is ready for consumption, use `get_query_results` to pull the final set. If you need a file download, `generate_download_link` creates a temporary pre-signed URL for an S3 resource. Always check what's available first by calling `list_mobility_datasets`. This ensures your agent knows exactly which dataset ID it needs before trying to execute any query.

Setup guide

Set up Veraset MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "veraset-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Veraset tools.",
)

result = await agent.run("List recent Veraset transactions")
print(result.output)

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Common questions about Veraset MCP in Pydantic AI

Use `get_query_status`. This tool checks the current progress of your SQL task and tells you when it's finished, so your agent can proceed to gather results.

Veraset manages mobility datasets. Your agent works with structured geospatial information and runs SQL queries against the column definitions found in these specific datasets.

Yep. Call `list_mobility_datasets`. This gives your agent a comprehensive inventory of every accessible dataset ID Veraset has for mobility data.

First, get the results with `get_query_results`. If you need it as a file, use `generate_download_link` to create a temporary S3 URL. It’s a two-step process.

Veraset manages mobility datasets. The schema exposes column definitions and various data types related to movement, which your agent can query and validate against Pydantic models.

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