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by ai.tulina.mcp
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apify_linkedin_profiles

Full LinkedIn profiles from their URLs — current title and company, experience, education, skills, location. No LinkedIn login needed.

harvestapi/linkedin-profile-scraper: check one person's current job, or refresh the titles of a list. About $4 per 1,000 profiles, $10 with the email search.

Returns — the LIST of profiles, one per URL found (or {error, run_id, status, items} for a run that did not succeed — billed too).

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_linkedin_profiles"
  }
}

Input Schema

NameRequiredDescriptionDefault
_orgNoOrg (id) de CET APPEL. `oto_list_orgs()`.
urlsYesprofile URLs (`https://www.linkedin.com/in/<id>`) or bare public identifiers. One run takes a batch: send them together.
_groupNoÉquipe (id) de CET APPEL, sous son org parente.
fieldsNokeep only these keys of each profile (they are wide), e.g. `["firstName", "lastName", "headline", "currentPosition", "linkedinUrl"]`.
_run_idNoOBLIGATOIRE 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.
_projectNoProjet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
_instanceNoInstance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.
with_emailNoalso search a professional email (2.5× the price).
max_total_charge_usdNohard 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 no annotations supplied, the description carries the full burden and does well: it discloses that no LinkedIn login is required, quantifies cost (~$4 per 1,000 profiles, $10 with email search), and warns that a failed run is still billed and returns an `{error, run_id, status, items}` object instead of the list. Rate limits, latency, and credential/connector prerequisites are not described.

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?

Front-loaded with the core capability, then cost, then return shape — each sentence adds information. The dash-separated fragments and the trailing parenthetical cost note are slightly dense but not padded.

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?

No output schema exists, so the description correctly specifies the return contract (a list, one profile per URL, or an error object) and the billing implication of failure. Given 9 parameters with full schema coverage, the remaining gap is only the tenant/run-scoping parameters and connector prerequisites.

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 the baseline is 3. The description does add real meaning — noting that profiles are wide and that `with_email` is a paid upgrade — but it does not explain the internal/tenant parameters (`_org`, `_project`, `_group`, `_run_id`, `_instance`) or the interaction between `fields` and `max_total_charge_usd`.

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?

States a specific verb and resource — full LinkedIn profiles fetched from profile URLs — and enumerates the returned data (title, company, experience, education, skills, location), plus names the underlying actor (harvestapi/linkedin-profile-scraper). It does not, however, differentiate itself from the many overlapping LinkedIn/enrichment siblings (linkedin_unipile_profile, fullenrich_enrich_linkedin, apollo_match_person), leaving the agent to infer which one to pick.

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?

"check one person's current job, or refresh the titles of a list" gives concrete use cases, and the batch semantics ("One run takes a batch: send them together") tell the agent how to call it efficiently. There is no explicit when-not guidance or named alternative for overlapping tools, so it falls short of 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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