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by io.apidirect

search_linkedin

Search LinkedIn posts. Provide a query and/or a filter below. Powerful filters: author (posts BY a person), author_title (posts by people with a given job title, e.g. Founder/CEO — applies alongside a query), author_company (posts by employees of a company id), from_company (posts by a company page id), mentions_company (posts that MENTION a company id), mentions_member (posts that mention a person), author_industry. Returns post content, engagement metrics, attached media, a has_content_entities repost flag, and optional AI sentiment.

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
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 per-page surcharge.
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. Dates show when Glama detected each change.

  1. 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"
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the return payload (post content, engagement metrics, attached media, has_content_entities repost flag, optional AI sentiment) and mentions a per-page surcharge for sentiment. It does not address rate limits, pagination behavior beyond what the schema offers, or the discrepancy around the 'author' filter.

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?

The description is compact and front-loaded, opening with the main purpose before explaining filters and return data. The filter list is packed with parenthetical explanations, making it dense but not bloated. The organization is logical, though the erroneous 'author' filter makes the otherwise crisp structure slightly misleading.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core operation, available filters, and return fields, which is fairly complete for a search tool. It lacks explicit mention of the sort_by and page parameters, does not explain that query is optional when a filter is provided, and omits guidance on how the 'author' item relates to the schema. Overall it is adequate but has noticeable gaps.

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 the baseline is 3; the description does add useful clarification, such as 'from_company (posts by a company page id)' and 'mentions_company (posts that MENTION)'. However, it also names an 'author' filter that is not present as an input parameter, which reduces trust and adds ambiguity rather than pure value.

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 opens with a specific verb and resource: 'Search LinkedIn posts.' It further clarifies scope by listing several distinct filters, which helps distinguish this from company/person/comment tools. However, it references an 'author' filter that does not appear in the input schema, creating some confusion about the exact tool surface.

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?

The description implies when to use the tool by saying to provide a query and/or a filter, and the filter list suggests it is for post-level search. It does not explicitly distinguish this from related siblings like linkedin_company_posts or linkedin_person_posts, nor does it state when not to use this tool. Usage guidance is present but mostly implicit.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.

Naming Consistency4/5

The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.

Tool Count2/5

74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.

Completeness4/5

The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.

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