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search_linkedin_jobs

Search LinkedIn job listings by keyword and location. Returns job title, company, location, seniority, and applicant count. No login needed.

Input Schema

NameRequiredDescriptionDefault
keywordsYesJob title or skill (e.g. "machine learning engineer")
locationNoLocation filter (e.g. "San Francisco", "Remote", "India")
maxResultsNoMax listings (default 25)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It usefully discloses return fields (title, company, location, seniority, applicant count) and that no authentication is required, but says nothing about rate limits, pagination, or result freshness.

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?

Three compact sentences, front-loaded with the action and scope, then return fields, then the auth note. Every sentence earns its place with no filler.

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 simple three-parameter search with no output schema, the description covers the action, filter axes, return fields, and the no-login precondition. It is largely self-sufficient, though pagination and result-count behavior go unmentioned.

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 all three parameters are already documented with examples. The description only echoes 'by keyword and location' and adds no format, defaulting, or filtering nuance beyond what the schema provides; 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?

States a specific verb (Search) and resource (LinkedIn job listings) with the filtering axes named. It is clearly distinct from siblings like search_linkedin_employees or search_linkedin_posts, but it never explicitly distinguishes itself from other job sources such as search_naukri_jobs or scrape_ats_jobs.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not-to-use guidance and no routing to alternatives. With siblings like search_naukri_jobs and scrape_ats_jobs covering similar job-search ground, an agent gets no help deciding which source to pick; only 'No login needed' hints at an operational precondition.

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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