Contact Extraction Grounding Data

Overview

Contact Extractor works by extracting contact data from a website URL using web scraping techniques. It returns the contact emails, phone numbers, and places found on the website.

Live Test Contact Extraction Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as ContactExtractionGroundingData. 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.

{
  "name": "ContactExtractionGroundingData",
  "arguments": {
    "url": "https://en.wikipedia.org/wiki/Email_address"
  }
}

You do not name the tool yourself; the model picks it. Asking about https://en.wikipedia.org/wiki/Email_address in the terms this source covers is enough for it to reach for ContactExtractionGroundingData 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"
    }
  }
}

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.

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.

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{
  "status": "ok",
  "error": null,
  "data": {
    "url": "https://en.wikipedia.org/wiki/Email_address",
    "emails": [
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]",
      "[email protected]"
    ],
    "phones": [],
    "places": [
      "China",
      "Japan",
      "Russia",
      "Rajasthan",
      "India"
    ],
    "emailCount": 27,
    "phoneCount": 0,
    "placeCount": 5,
    "uniqueDomains": [
      "example.com",
      "example.org",
      "s.example",
      "EasierReading.org",
      "pobox.com"
    ]
  }
}

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.

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 emailCount: 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.

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

Sales Prospecting Enrichment

Sales reps scan company contact pages to extract direct support emails and phone numbers before launching targeted outbound calling campaigns.

Local Directory Aggregation

When curating regional merchant directories, indexing bots scrape storefront websites to verify physical street addresses and telephone lines.

Vendor Onboarding Verification

Before approving newly registered suppliers, procurement portals check listed corporate websites to confirm published business locations and operational telephone numbers.

Talent Sourcing Workflows

To build candidate pipelines, recruiting platforms scrape agency team pages to discover public email addresses for key department heads.

Other ways to use Contact Extraction Grounding Data

Set up Contact Extraction 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.

Related

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