Tulina

apify_website_contacts

Contact details published on company websites — emails, phones, and LinkedIn / Facebook / Instagram / X / YouTube links. One merged row per site. vdrmota/contact-info-scraper.

Find a company's LinkedIn page or generic email from its website. ~$0.30 for 50 sites with the defaults. More than ~30 sites can exceed the 300 s wait: the run keeps going, pick it up with apify_run_status then apify_dataset_items (the error names the run).

Returns — the LIST of sites with emails, phones, linkedIns… (or {error, run_id, status, items}).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "urls": "<urls>"
    },
    "name": "apify_website_contacts"
  }
}

Input Schema

Name	Required	Description
`_org`	No	Org (id) de CET APPEL. `oto_list_orgs()`.
`urls`	Yes	website URLs (`https://www.example.com`).
`_group`	No	Équipe (id) de CET APPEL, sous son org parente.
`fields`	No	keep only these keys, e.g. `["domain", "emails", "linkedIns"]`.
`_run_id`	No	OBLIGATOIRE à chaque appel dès `run_start` ou `data_claim_next` : sans lui, l'écriture sur une ligne réservée est refusée. Hérité seulement si le serveur tient un run actif — n'y compte pas.
`_project`	No	Projet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
`_instance`	No	Instance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.
`max_depth`	No	link depth from each start page (1 = the home page and the pages it links to — contact, about…).
`max_pages_per_site`	No	pages read per site at most.
`max_total_charge_usd`	No	hard cost ceiling of the run, $5 by default, at most your organization's ceiling per run ($25 unless set otherwise).

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With zero annotations, the description carries the full burden and does well: it discloses approximate cost (~$0.30 per 50 sites with defaults), the 300s wait limit and that the run continues past it, and the shape of an error result ({error, run_id, status, items}). It does not state permission requirements or whether the operation writes anything, but for a read-style scraper this is close to complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Roughly five short lines, front-loaded with the purpose before cost and async guidance. Some fragmentation (stray line breaks, 'vdrmota/contact-info-scraper.' as a bare fragment) and a slightly awkward nested return sentence cost it a point, but little is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so describing the per-site row shape (emails, phones, linkedIns) and the error envelope is genuinely necessary and present. Async continuation and cost ceilings are also covered. What is missing is the relationship to other Apify tools for input-side setup, but the core call-and-collect loop is fully specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter including _run_id, max_depth, max_pages_per_site and max_total_charge_usd is already documented in structured data. The description only adds that cost figures assume 'the defaults' and lists expected output keys, which does not meaningfully extend parameter meaning. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb and resource ('Contact details published on company websites — emails, phones, and LinkedIn / Facebook / Instagram / X / YouTube links') and names the underlying actor (vdrmota/contact-info-scraper). It is clearly distinguishable from siblings like apify_linkedin_companies, though it never explicitly contrasts with apify_website_crawl, the closest alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a concrete use case ('Find a company's LinkedIn page or generic email from its website') and, unusually, tells the agent what to do when the run exceeds the 300s wait — pick it up with apify_run_status then apify_dataset_items. It lacks an explicit 'don't use this for X, use sibling Y instead' exclusion, which keeps it below a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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