How to Use the Veraset MCP in Claude Code

Q: How do I start an automated query pipeline against Veraset using Claude Code?

Your script executes executesqlquery, passing the necessary dataset ID and SQL. The MCP Server then returns a job ID, which you can use to monitor progress with getquerystatus.

Q: If a scheduled Veraset query fails, how do I diagnose it using Claude Code?

You check the status first with getquerystatus. If the failure is due to an active task, you can use cancelrunningquery before retrying the job in your pipeline.

Q: Does this MCP Server support multiple mobility datasets for Veraset?

Yes. You first run listmobilitydatasets to get a full inventory of available dataset IDs, allowing you to target any source needed by the script.

Q: What is the best way to handle large result sets from Veraset in Claude Code?

Use getqueryresults. It supports pagination, meaning your script can loop through and collect all rows without hitting memory limits. You'll get every row eventually.

Run scheduled, headless data pipelines with Claude Code: Query Veraset's mobility datasets via the terminal.

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Create your Vinkius account to connect Veraset to Claude Code and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Veraset with Claude Code

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Automating Data Queries for CI/CD

When running in a pipeline, you call `execute_sql_query` directly. It needs a dataset ID and ANSI SQL. The MCP Server returns a query ID that your script must pass to `get_query_status` periodically until the job completes.

Scheduled Data Drop Management

For cron jobs, you can list expected storage locations using `list_s3_delivery_folders`. You also check available inputs with `list_mobility_datasets`, making sure your script knows which dataset ID to target.

Handling Data Retrieval and Cleanup

After the query runs, you use `get_query_results` in a loop to pull all rows. If external systems need the data, `generate_download_link` creates the temporary S3 URL for secure piping.

Setup guide

Set up Veraset MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see veraset-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Veraset transactions." It will automatically discover and invoke the available Veraset tools.

Terminal

claude mcp add --transport http veraset-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Veraset MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "veraset-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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.

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Common questions about Veraset MCP in Claude Code

Your script executes `execute_sql_query`, passing the necessary dataset ID and SQL. The MCP Server then returns a job ID, which you can use to monitor progress with `get_query_status`.

You check the status first with `get_query_status`. If the failure is due to an active task, you can use `cancel_running_query` before retrying the job in your pipeline.

Yes. You first run `list_mobility_datasets` to get a full inventory of available dataset IDs, allowing you to target any source needed by the script.

Use `get_query_results`. It supports pagination, meaning your script can loop through and collect all rows without hitting memory limits. You'll get every row eventually.

The server manages mobility datasets and the resulting structured SQL query results. This allows your DevOps automation to process core Veraset analytics data types.

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