Spam Detection Grounding Data

Overview

Spam Detector works by analyzing the text provided and detecting if it is spam. It uses advanced algorithms to analyze the text and return the likelihood of spam.

Live Test Spam Detection Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as SpamDetectionGroundingData. 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": "SpamDetectionGroundingData",
  "arguments": {
    "text": "Congratulations! You have won a free iPhone. Click here to claim your prize now!"
  }
}

You do not name the tool yourself; the model picks it. Asking about Congratulations! You have won a free iPhone. Click here to claim your prize now! in the terms this source covers is enough for it to reach for SpamDetectionGroundingData on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source 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": {
    "vervecontext": {
      "url": "https://api.vervecontext.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
`text`Required	string	The text to detect spam in length 0–50000
`email`Optional	string	The email address to validate against the spam database. This is optional email
`ip`OptionalPremium	string	The IP address to validate against the spam database. This is optional ip

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": {
    "likelySpam": true,
    "isDisposableEmail": false,
    "isIPBlacklisted": false,
    "ipDetails": {
      "country": "IN",
      "region": "DL"
    },
    "parsed": true
  }
}

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
`likelySpam`	boolean	`true`	Indicates whether the text is likely spam
`isDisposableEmail`Premium	boolean	`false`	Indicates if email address uses disposable email service
`isIPBlacklisted`Premium	boolean	`false`	Indicates if IP address is on malicious blocklist
`ipDetails`	object	`{…}`	Geolocation of the supplied IP address, or null when no IP was given or it could not be located
`ipDetails.country`Premium	string	`IN`	Country code where IP address is geographically located
`ipDetails.region`Premium	string	`DL`	Region or state where IP address is located
`parsed`	boolean	`true`	Indicates whether input data was successfully parsed

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at likelySpam: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

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 source is not part of VerveContext. Check the catalog.
`429`	Out of credits, or a brief rate limit. The message tells them apart.

A call costs 10 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Forum Comment Screening: Community platforms evaluate incoming forum posts and blog comments to hold suspected spam for moderator review before it appears publicly.
Contact Form Filtering: When visitors submit inquiry forms, lead capture systems check the message text and sender email to drop junk leads automatically.
Chat Message Moderation: Flag suspicious direct messages in real time to stop promotional bots from spamming newly registered members.
Job Listing Verification: To prevent affiliate link abuse, job boards screen incoming vacancy descriptions before listing them in search results.

Other ways to use Spam Detection Grounding Data

Set up Spam Detection Grounding Data on VerveContext, or reach the same source a different way. Your VerveContext 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 →

Give it to an AI agentConnect over MCP and your agent calls it as a native tool — Claude, Cursor, ChatGPT.VerveKit →Reference →

Google Sheets or ExcelA =VERVE() formula fills a column — no script, no export, recalculates in place.VerveSheets →Reference →