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Search LinkedIn posts

linkedin_search_posts
Read-onlyIdempotent

Search LinkedIn posts. 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".
sortOrderNoResult ordering: "date_posted" for newest-first, "relevance" for LinkedIn ranking.
datePostedNoRestrict to posts published within a recent window.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the read-only sentence is largely confirmation. The genuinely additive disclosure is the pricing model — 1 credit per result with a 1-credit minimum per request — which is cost information an agent cannot get from any structured field and which affects how it should size the `count` parameter.

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

Conciseness4/5

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

Three short sentences, front-loaded with purpose, then safety, then cost — nothing is buried. The middle sentence restates the readOnlyHint annotation almost verbatim, which is minor redundancy but not wasteful enough to undercut the structure.

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 five fully documented parameters, no output schema, and annotations covering the safety profile, the description supplies the one missing operational fact: cost per request. It does not describe what a returned post contains or how pagination via `start` behaves, but there is no output schema to lean on, leaving a small gap.

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%, with every parameter including `count`, `start`, `sortOrder`, `datePosted`, and `keyword` documented inline, including enums and ranges. The description adds no parameter-level 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?

The description gives a specific verb and resource ("Search LinkedIn posts"), so an agent immediately knows the operation. It does not, however, distinguish this tool from close siblings such as linkedin_search_all, linkedin_get_posts_from_company, or linkedin_get_posts_from_profile, which could also surface posts.

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?

Usage is only implied by the verb "Search" — there is no explicit statement of when this keyword-based post search is preferred over linkedin_search_all or the profile/company post-listing tools. No exclusions, prerequisites, or alternative names are provided.

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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