Veraset MCP for AI. Analyze Location Data and Manage S3 Storage
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Veraset connects your AI agent directly to billions of points of interest and mobility datasets. You can run complex geospatial SQL, inspect dataset structures, monitor long-running queries, and generate secure download links for massive location data drops.
What your AI can do
Cancel running query
Immediately stops an SQL query that is currently running in the background.
Execute sql query
Starts a new, complex SQL task against Veraset's massive dataset pool.
Generate download link
Creates a temporary, secure URL for downloading data stored in S3 buckets.
Send complex ANSI SQL queries to calculate specific metrics across mobility datasets.
Retrieve the column definitions and data types for any available dataset before writing a query.
Fetch quick samples of rows to confirm the expected format and content of a dataset.
Check the real-time status, progress percentage, and total bytes scanned for long-running queries.
Pull the final result rows from a completed query or immediately abort an intensive, stalled job.
Create temporary, secure links for bulk downloads of structured data delivered to S3.
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Veraset: 10 Tools for Geospatial Analysis
These tools allow you to discover datasets, validate structures, execute massive SQL calculations against Veraset's cloud cluster, and manage the resulting data files.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Veraset on VinkiusCancel Running Query
Immediately stops an SQL query that is currently running in the background.
Execute Sql Query
Starts a new, complex SQL task against Veraset's massive dataset pool.
Generate Download Link
Creates a temporary, secure URL for downloading data stored in S3 buckets.
Get Dataset Metadata
Retrieves high-level technical information about an entire mobility dataset package.
Get Query Results
Retrieves the final result rows from an SQL query that has already finished...
Get Query Status
Checks and reports the current status, progress percentage, and estimated completion time of a running job.
Get Dataset Sample
Provides a small, immediate preview of the first few rows in any given dataset.
Get Dataset Schema
Returns the precise column names and data types for a specific mobility dataset.
List Mobility Datasets
Identifies all available mobility dataset packages that Veraset has provisioned for...
List S3 Delivery Folders
Lists the specific S3 cloud folders where scheduled data drops are delivered to your...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Manual Data Discovery Process
Right now, figuring out what data is ready for analysis means clicking through several dashboards. You're checking one tab for dataset names, then opening another to view the schema, and finally writing code in a third place just to see if the column you need actually exists. It’s slow, it involves copy-pasting dozens of field names, and it takes hours just to set up the query.
With this MCP, your agent handles all that boilerplate work for you. You simply tell it what data points you want—like 'all movement signals'—and it instantly pulls the necessary metadata or a sample preview. The result is that you move straight from asking a question to getting usable results.
Managing Data Retrieval with `get_dataset_schema`
Without this tool, if the dataset provider changes a column name—say, changing 'Store ID' to 'Location Identifier'—your entire data pipeline breaks. You waste time debugging why your query returned zero results and then spend another hour hunting through documentation just to find the new field name.
Using `get_dataset_schema` means you ask for the exact definition of every column, including its type (is it a string? is it a float?). This gives you absolute certainty about the data structure before you write one line of code. It's rock solid.
What your AI can actually do with this
This MCP lets you treat huge, raw geolocation datasets like a local database. Instead of having to export data into your own environment just to run an aggregate query, your agent handles the SQL execution directly against Veraset's cloud cluster. You can ask it to find patterns—like identifying POI clusters or tracking movement flow across cities—and get immediate results without context switching.
If you’re building automated pipelines that rely on this location intelligence, Vinkius AI Analytics provides full visibility into the entire process. You always know exactly which datasets are being queried and how much of your budget is tied up in the background job runs. This makes managing complex data flows reliable.
You'll use it to inspect dataset schemas first, then fire off custom SQL queries that compute geolocation aggregates on demand. Finally, when the math is done, you generate a temporary, secure link so you can download the resulting tables for final analysis.
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Here's how it actually works
The bottom line is: You guide your agent through discovering data structure, running the calculation in the cloud, and finally retrieving/saving the finished result.
First, ask your agent to list the available datasets or inspect a specific dataset's schema using get_dataset_schema to figure out what fields you can query.
Next, instruct it to construct and execute the full SQL logic via execute_sql_query. If the job takes time, use get_query_status to track its progress. You’ll get a unique Job ID back.
Once the status shows 'Completed,' ask for the results using get_query_results, or if you need permanent files, generate a secure link with generate_download_link.
Who is this actually for?
Data Scientists who are sick of context switching between their IDE and a massive database console. Also, Retail Strategy Leads who need to know why foot traffic dropped last Tuesday without building an entire data warehouse.
They run get_dataset_schema first, then write and test complex SQL queries using execute_sql_query, validating the output with get_query_results before packaging it for a model.
They use this to validate subset queries on movement datasets, ensuring the logic works correctly by running sample checks and using cancel_running_query if an aggregation job hangs.
They ask their agent for quick summaries of competitive POI foot-traffic anomalies, relying on the system to handle the heavy lifting across multiple datasets and then requesting a download link using generate_download_link.
What Changes When You Connect
Stop guessing what data you have. Use list_mobility_datasets to see every available package, saving time spent clicking through confusing developer consoles.
Before writing a single line of SQL, check the structure using get_dataset_schema. This saves massive headaches when column names change or types are unexpected.
Need to prove your query works? Use get_dataset_sample first. You get an instant view of ten rows so you know exactly what kind of data you’re about to run against.
The process is messy sometimes, especially big jobs. Instead of waiting blindly, use get_query_status to track the progress and see if the job is stalled or still running.
When your query finishes, don't manually copy-paste results. Use get_query_results for paginated access, or generate_download_link to get a single secure file you can use immediately.
See it in action
Figuring out what data is available
A new analyst needs to know if Veraset has movement data for the last quarter. They simply ask their agent to run list_mobility_datasets, getting a list of all accessible packages without having to navigate complex AWS or internal portal menus.
Validating raw column names
A geospatial engineer needs to write a join query but isn't sure if the 'accuracy' field is stored in meters or feet. They run get_dataset_schema on the target dataset, confirming the data type and unit before writing any code.
Getting final structured reports
A retail lead runs a complex aggregation query to calculate total foot traffic per store ID. Once the job is done, they use get_query_results to pull the exact table needed for their presentation slide deck.
Getting data into an external system
The team has completed a massive analysis and needs the raw parquet files. They instruct their agent to call generate_download_link, getting a temporary, time-limited URL they can pass directly to their BI tool.
The honest tradeoffs
Assuming data exists
Telling the agent to write complex SQL against 'the best location dataset' without checking if it’s available or what its schema is.
Always start by running list_mobility_datasets first. Then, validate the structure using get_dataset_schema. This ensures your query targets a real, known field.
Running massive queries and forgetting them
Firing off an intensive job via execute_sql_query and then walking away without checking if it's still running or if it failed silently.
Immediately follow the execution with a call to get_query_status. This confirms the job is active, shows its progress percentage, and tells you exactly when you can expect results.
Trying to download data before calculation
Asking for a download link (generate_download_link) for metrics that haven't been calculated yet.
You must first use execute_sql_query to run the math. Only after confirming the job status is complete can you generate a stable download link.
When It Fits, When It Doesn't
Use this MCP if your core need involves running complex, multi-step SQL calculations against massive, structured geospatial datasets (e.g., 'find all stores where foot traffic dropped 20% between Q1 and Q2'). It's built for the deep data scientist workflow—you're inspecting schema with get_dataset_schema, executing computations via execute_sql_query, and then managing the job lifecycle using get_query_status. Don't use it if you just need to look up a simple record, like checking a single user ID. For that, a standard relational database connector is better. If you only need basic file listing without querying, stick with list_s3_delivery_folders and skip the SQL steps entirely.
Questions you might have
How do I find out what Veraset datasets are available using `list_mobility_datasets`? +
The agent calls list_mobility_datasets and returns a list of all dataset packages you have access to. This tells you the names you need before running any queries.
What happens if my SQL query is too big for Veraset? Can I cancel it using `cancel_running_query`? +
Yes, if a job is taking longer than expected or hitting limits, you use get_query_status first to verify the progress, and then issue cancel_running_query to abort the task.
Can I get metadata for an S3 bucket using this MCP? +
No. While you can list available S3 folders with list_s3_delivery_folders, you must use a separate tool or process to manage the actual cloud storage structure.
After running an SQL query, how do I get the final data out? +
You first check the status with get_query_status. Once it's done, you either request the results using get_query_results or ask for a permanent file link via generate_download_link.
Before I run a complex query, how do I check the column definitions using `get_dataset_schema`? +
You use get_dataset_schema to pull the technical definition of any dataset. This instantly shows you all available columns and their data types (like text, date, or float). It's crucial for validating your SQL syntax before running a job.
I want to quickly validate if a dataset is relevant without querying everything; how does `get_dataset_sample` help? +
get_dataset_sample retrieves the first few rows of data, giving you an immediate look at what it actually contains. This lets you confirm the format and general quality of the location records before writing a full aggregation query.
If my SQL job is running for hours, how do I check its progress using `get_query_status`? +
get_query_status allows you to track long-running jobs without re-executing the query. It reports metrics like percentage completion and total bytes scanned, letting you know if the process is still active.
After I pull results using `get_query_results`, how do I generate a secure, permanent download link with `generate_download_link`? +
generate_download_link creates a temporary, pre-signed URL for bulk downloads. This is the easiest way to grab the full data set into your own environment without having to manually export it.
Can the AI really create and download a pre-signed link from Veraset's S3 directly? +
Yes. Upon using the generateDownloadLinkTool, the agent will interface via Veraset's protocol using your API token, instantly retrieving an authenticated, time-sensitive download link to let you extract the immense dataset files securely.
What happens if a SQL statement to Veraset starts taking too long? +
You don't need to panic or swap tools. Instruct your agent: cancel the running query id 'query-xx9', and the cancelQueryTool fires an immediate abort network call. The computation ends, saving extensive costs without abandoning the conversational flow.
How can I preview geolocation signals before compiling expensive queries? +
Ask for the getSchemaTool followed by getSampleTool. The AI perfectly delivers the dataset definitions and outputs five physical preview rows right inside your message history, confirming expected structure formatting.
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