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Search LinkedIn products

linkedin_search_products
Read-onlyIdempotent

Find software products listed on LinkedIn by keyword. Each result includes the product name, category, maker, description, top features, integrations and the companies that use it. 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

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, and the description adds real value beyond them: it names the public-data scope and, more importantly, the cost model ('1 credit per result returned, with a minimum of 1 credit per request'), which no annotation conveys. It says nothing about pagination expectations or failure handling, keeping it short of a 5.

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 tight sentences, front-loaded with the action and scope, then result contents, then safety and cost. Every sentence carries information an agent needs, and nothing is padded.

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?

With no output schema, the description usefully enumerates the returned fields (name, category, maker, description, features, integrations, companies), and discloses cost and read-only behavior for a simple three-parameter tool. It stops just short of fully complete since it does not address result ordering or how pagination interacts with the per-result credit cost.

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 all three parameters, including bounds and defaults for count and start, are documented there. The description mentions only 'keyword' and adds no format or range detail beyond the schema, so the baseline 3 applies.

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 first sentence gives a specific verb ('Find'), resource ('software products listed on LinkedIn'), and input ('by keyword'), which separates it cleanly from siblings like linkedin_search_companies, linkedin_search_services, and linkedin_search_jobs.

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

Usage Guidelines3/5

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

Usage is implied by naming the resource, but there is no explicit when-to-use or when-not-to-use guidance, and no sibling (e.g. linkedin_search_all or linkedin_search_services) is named as an alternative.

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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