Tulina

linkedin_unipile_facets

Resolve a LinkedIn filter NAME into {id, name} candidates to pass to linkedin_unipile_search. To be used BEFORE a structured search as soon as a criterion is not a simple keyword (skill, industry, location, employer…).

Choosing the right candidate is YOUR job: a single input often returns several facets ("Microsoft Excel" → Excel, Microsoft Office, …) — read the names and keep the relevant id. Then pass it to linkedin_unipile_search (location/company/industry already accept ids; the other facets are coming — see guide linkedin-search).

Returns {facet_type, candidates: [{id, name}]}. Resolution INDEPENDENT of the product/contract (works even outside Recruiter/Sales Nav).

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": {
      "facet_type": "<facet_type>",
      "keywords": "<keywords>"
    },
    "name": "linkedin_unipile_facets"
  }
}

Input Schema

Name	Required	Description
`_org`	No	Org (id) de CET APPEL. `oto_list_orgs()`.
`limit`	No	max number of candidates (default 25).
`_group`	No	Équipe (id) de CET APPEL, sous son org parente.
`_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.
`_account`	No	Compte à OPÉRER quand plusieurs existent. `oto_identity(op='list')`.
`_project`	No	Projet (id) de CET APPEL — jeton PRIMAIRE. `oto_project(op='list')`.
`keywords`	Yes	the label to resolve (e.g. "Microsoft Excel", "Paris").
`_instance`	No	Instance de connecteur PRÉCISE (un credential exact). `oto_instance(op='list')`.
`facet_type`	Yes	facet type, UPPERCASE. Confirmed: `SKILL`, `LOCATION`, `INDUSTRY`, `COMPANY`. Others exist (try `TITLE`, `SCHOOL`, `FUNCTION`, `SENIORITY`, `LANGUAGE`…) — an invalid type raises an `Expected kind 'StringEnum'` error.

Schema Changelog

Changes observed during successful MCP inspections.

No schema history has been recorded yet.

TDQS

A4.5/5.0

Behavior4/5

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

No annotations are supplied, so the description bears the full disclosure burden; it delivers by stating the return shape (`{facet_type, candidates: [{id, name}]}`), the multi-candidate ambiguity risk with a concrete example, and that resolution is independent of product/contract (works outside Recruiter/Sales Nav). It does not mention auth, rate limits, or whether resolution is cached/live.

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?

Purpose and the 'use before search' rule are front-loaded, and every clause carries information. The line breaks and stacked parentheticals (guide reference, 'coming' facets) make it slightly scattershot, but nothing is truly redundant.

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?

For a 9-parameter resolver with no annotations and no output schema, the description supplies purpose, invocation timing, return structure, and a failure mode. Remaining gaps are secondary (no note on whether lookups cost credits or are cached), and the `_org`/`_group`/`_run_id` scaffolding params are left entirely to the schema.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds cross-tool semantics the schema cannot: which resolved ids map onto which `linkedin_unipile_search` parameters, and that `keywords` is a human label rather than an id. It does not add format detail for `facet_type` beyond the schema, which already lists valid and speculative values.

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

Purpose5/5

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

The description opens with a specific verb+resource: 'Resolve a LinkedIn filter NAME into `{id, name}` candidates', and explicitly names the consumer tool `linkedin_unipile_search`. It distinguishes itself from a keyword search by scoping itself to non-keyword criteria (skill, industry, location, employer).

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

Usage Guidelines5/5

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

Timing is explicit: 'BEFORE a structured search as soon as a criterion is not a simple keyword'. It also names the downstream handoff (`location`/`company`/`industry` accept ids, other facets still pending) and warns that multiple candidates may come back and disambiguation is the caller's responsibility.

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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