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LinkedIn: Recruiter list applicants

linkedin_recruiter_list_applicants
Read-onlyIdempotent

List applicants from a LinkedIn Recruiter Hiring Project talent pool. Resolve project_id first. Recruiter applicants are project-scoped; never substitute a Classic job_id for project_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNoOpaque next_cursor returned by previous page; never invent.
filtersNoRecruiter talent-pool applicant filters such as sort/seniority/current-company using documented V2 enums/parameter IDs.
account_idNoOptional Nilyo connection ID (unipile_account_id from list_connected_accounts). Omit when the user has one account for this provider. When several exist, Nilyo never guesses: list them (display name, identifier, provider user ID), choose the one the user named or ask, and pass its ID here.
project_idYesExact LinkedIn Recruiter Hiring Project ID from linkedin_recruiter_list_projects. Never pass project name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already cover read-only and non-destructive/idempotent behavior; the description adds the project-scoped constraint and the resolve-first prerequisite. This is useful but not a rich disclosure of pagination, return shape, or provider-side effects.

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

Conciseness5/5

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

Two tight sentences place the action first, state the prerequisite, and add the critical warning without redundancy.

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?

The most important contextual trap—project_id versus Classic job_id—is explicitly addressed, and the schema covers account disambiguation and pagination. Given five parameters and no output schema, a brief note on return data would push this to a 5.

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 80%, so the schema already explains project_id, cursor, filters, and account_id. The description reinforces project_id exactness but does not add new meaning for limit, cursor, filters, or account selection.

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 states a specific verb ('List'), a resource ('LinkedIn Recruiter Hiring Project talent pool'), and explicitly contrasts Recruiter applicants with Classic job_ids, making it easy to distinguish from linkedin_classic_list_job_applicants.

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?

It tells the agent to resolve project_id first and warns never to substitute a Classic job_id, which is a clear when-not signal. It stops short of naming the alternative tool explicitly, so it misses a full 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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