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Search LinkedIn Posts

search_linkedin
Read-only

Search LinkedIn posts by keyword and/or filters: author (posts by a person), author_title (posts by people with a job title, e.g. Founder), author_company (posts by a company's employees), from_company (posts by a company page), mentions_company or mentions_member (posts mentioning a company or person), author_industry. Returns post text, author, date, engagement metrics, attached media and a has_content_entities flag (true when an image, article, video or job is attached). Company ids come from search_linkedin_companies. 20 posts per page. Billed $0.006 per request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 1-25 (default: 1). 20 posts per page
queryNoSearch keyword (max 500 characters). Optional if you supply a filter below.
authorNoPosts authored by this person — profile URL, public slug (e.g. williamhgates), or member URN. Comma-separate for multiple.
sort_byNoSort order: "most_recent" or "relevance"most_recent
posted_agoNoOnly posts from this period: "24h", "7d", or "30d" (default: all time). Works with both sort orders and every filter
author_titleNoPosts by authors whose job title matches this free text (e.g. "CEO", "Founder"). Applies alongside a query.
from_companyNoPosts authored by a company page. Numeric company id(s), comma-separated.
get_sentimentNoAdd AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small surcharge per page, or per request on single-request endpoints.
author_companyNoPosts by people who work at this company. Numeric LinkedIn company id (from search_linkedin_companies).
author_industryNoPosts by authors in these numeric LinkedIn industry id(s), comma-separated. Advanced; applies alongside a query.
mentions_memberNoPosts that mention this person (profile URL, public slug, or member URN).
mentions_companyNoPosts that mention this company. Numeric company id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / posted_ago
      Added value: +{
+  "description": "Only posts from this period: \"24h\", \"7d\", or \"30d\" (default: all time). Works with both sort orders and every filter",
+  "enum": [
+    "24h",
+    "7d",
+    "30d"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changed
    • changedInput schema / properties / get_sentiment / description
      Previous value: -"Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge."New value: +"Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small surcharge per page, or per request on single-request endpoints."
  3. Changed1 schema field changed
    • changedInput schema / properties / page / description
      Previous value: -"Page number for pagination (default: 1)"New value: +"Page number, 1-25 (default: 1). 20 posts per page"
  4. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: 20 posts per page (max 25 pages implied by schema), a $0.006 per request billing note, and the return shape including the has_content_entities flag. It stops short of describing rate limits or auth requirements.

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?

Front-loaded with the core verb and scope, then a dense enumeration of filters, return fields, id source, pagination and pricing. Every clause carries information, though the single long paragraph is heavier than ideal for scanning.

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 correctly steps in to describe return fields and the has_content_entities flag, and it covers pagination and billing. For a 12-param, all-optional tool it is largely complete, though it omits how multiple filters combine (AND semantics) and any auth/pricing caveats beyond the per-request cost.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds meaning by grouping and contrasting filters (author vs author_company vs from_company vs mentions_*) in a way the flat schema does not. It also clarifies intent-level distinctions (e.g. author_title is free text like 'Founder') that aid filter selection.

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 and resource (search LinkedIn posts) and enumerates the filter dimensions with their distinct meanings, notably separating author_company (a company's employees) from from_company (a company page) so an agent can pick the right filter. It does not, however, name or differentiate itself from close siblings like linkedin_person_posts or linkedin_company_posts.

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 through the filter list, and it helpfully points out that company ids come from search_linkedin_companies, which is real routing value. But there is no explicit when-to-use-this-vs-alternatives guidance, e.g. no statement on when to prefer this over linkedin_person_posts or linkedin_post_details.

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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