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LinkedIn: Search jobs

linkedin_search_jobs
Read-onlyIdempotent

Search LinkedIn jobs with the user's own account. Use for job discovery by keywords, title, company, location, seniority, employment status or workplace type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNo
job_titleNo
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.
date_postedNo
primary_locationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide readOnly/openWorld/idempotent hints, so the safety profile is covered. The description adds the auth framing 'with the user's own account,' which is useful context, but it does not disclose result format, pagination, or any rate-limit behavior.

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 short sentences with no filler, the primary action and search scope are front-loaded, and the usage guidance follows immediately. Every word contributes.

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

Completeness3/5

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

The tool has five optional parameters and no output schema, so more detail on expected results or parameter formats would help. The schema does describe account_id thoroughly, and annotations cover side effects, but pagination, result shape, and the mismatch between described facets and actual parameters remain gaps.

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?

With only 20% schema description coverage, the description partially compensates by naming search criteria, but several mentioned facets (company, seniority, employment status, workplace type) do not map to actual schema properties. It adds meaning for keywords and title but leaves date_posted and primary_location semantics mostly to their names.

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 clearly states a specific verb and resource: 'Search LinkedIn jobs'. It also frames the search facets (keywords, title, company, location, seniority, employment status, workplace type), which distinguishes this from sibling search tools like linkedin_search_people and linkedin_search_companies.

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?

The phrase 'Use for job discovery' gives explicit usage context, and listing search criteria tells an agent when this tool fits. It does not name alternatives or state when not to use it, so it stops short of full routing guidance.

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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