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Search LinkedIn courses

linkedin_search_courses
Read-onlyIdempotent

Search LinkedIn courses. Read-only: returns public data and changes nothing. Costs 1 credit per result returned, with a minimum of 1 credit per request.

Input Schema

NameRequiredDescriptionDefault
countYesNumber of results to return (1–10). Defaults to 10.
startNoNumber of results to skip. Defaults to 0.
keywordYesKeyword(s) to search LinkedIn for, e.g. "openai", "software engineer", "fintech".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered and restating "read-only" earns little. The description does add genuinely new behavior: a credit cost of 1 per result with a 1-credit minimum per request, which affects how an agent should set the count parameter. It stops short of describing pagination or result contents.

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 short sentences with zero redundancy: purpose first, then safety, then cost. Each sentence carries distinct information an agent needs before calling the tool.

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 three-parameter read-only search with full schema coverage and rich annotations, the description covers purpose, safety and a cost model that the structured fields do not express. The remaining gap is minor: it does not say what a returned course record looks like or whether the credit cost interacts with the start offset for pagination.

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%, and each parameter (keyword, count, start) is documented with type, bounds and an example in the schema itself. The description adds no meaning beyond that, so the baseline of 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?

"Search LinkedIn courses" gives a specific verb plus a specific resource, so the agent can tell it apart from linkedin_search_jobs or linkedin_search_companies by subject matter. It does not explicitly differentiate itself from the broader linkedin_search_all, which is the main ambiguity in a dense sibling namespace.

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?

The description states no condition for choosing this tool over linkedin_search_all or any other linkedin_search_* sibling, and offers no exclusions or prerequisites. Usage is only implied by the word "courses".

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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