User Agent Generator Skill

Overview

User Agent Generator works by generating a random user agent string based on the browser, operating system, and device. It returns the generated user agent string in a structured format.

Live Test User Agent Generator Skill Skill →

The tool

Once your client is connected to the VerveKit server, this appears in its tool list as UserAgentGeneratorSkill. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

Tool call

{
  "name": "UserAgentGeneratorSkill",
  "arguments": {}
}

Connecting

One server URL covers every skill in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervekit": {
      "url": "https://api.vervekit.com/v1/mcp"
    }
  }
}

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

Argument	Type	Description
`device`Optional	string	The device type for the user agent string `desktopmobiletablet` default desktop

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

Result

{
  "status": "ok",
  "error": null,
  "data": {
    "ua": "Mozilla/5.0 (Windows NT 6.4) AppleWebKit/537.36.0 (KHTML, like Gecko) Chrome/94.0.0.0 Safari/537.36.0",
    "browser": {
      "name": "Chrome",
      "version": "94.0.0.0",
      "major": "94"
    },
    "engine": {
      "name": "Blink",
      "version": "94.0.0.0"
    },
    "os": {
      "name": "Windows",
      "version": "10"
    },
    "device": {},
    "cpu": {}
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Field	Type	Example	Description
`ua`	string	`Mozilla/5.0 (Windows NT 6.4) AppleWebKit/537.36.0 (KHTML, like Gecko) Chrome/94.0.0.0 Safari/537.36.0`	Complete user agent string representing browser environment
`browser`	object	`{…}`
`browser.name`	string	`Chrome`	Name of the browser (e.g., Chrome, Firefox, Safari)
`browser.version`	string	`94.0.0.0`	Full browser version number including all segments
`browser.major`	string	`94`	Major version number of the browser
`engine`	object	`{…}`
`engine.name`Premium	string	`Blink`	JavaScript engine name (e.g., Blink, WebKit, Gecko)
`engine.version`Premium	string	`94.0.0.0`	JavaScript engine version number
`os`	object	`{…}`
`os.name`	string	`Windows`	Operating system name (e.g., Windows, macOS, Linux)
`os.version`	string	`10`	Operating system version number
`device`Premium	object	`{…}`	Device information object containing vendor and model details
`cpu`Premium	object	`{…}`	CPU architecture and processor information

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

Status	What it means
`400` / `422`	The arguments did not validate. The message names the offending one.
`401`	The OAuth session is invalid or expired — reconnect the server.
`403`	Blocked by a key restriction or an IP allow-list. Never a bad identity.
`404`	This skill is not part of VerveKit. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Web Scraping Rotation: Rotate header strings across crawler requests by passing mobile or desktop targets to mimic different visitors and reduce detection.
Cross Browser QA: QA engineers feed randomized browser versions and operating systems into automated test suites to verify responsive UI rendering without physical devices.
Analytics Parser Benchmarking: To test telemetry parsers, supply generated strings into internal pipelines to check whether software accurately detects incoming browser and operating system families.
Honeypot Client Simulation: When staging decoy network services, configure mock client sessions with realistic tablet and desktop signatures to inspect incoming bot behavior.

Other ways to use User Agent Generator Skill

Set up User Agent Generator Skill on VerveKit, or reach the same source a different way. Your VerveKit account and credits work on all of them — one key, one balance.

Call it as a REST APIOne HTTPS endpoint and an x-api-key header, with SDKs for Node, Python and .NET.APIVerve →Reference →