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API Direct

by io.apidirect

linkedin_person_posts

Get recent posts authored by a LinkedIn person by profile URL or public slug. Returns posts with engagement metrics (likes, comments, shares, reactions), author info, images, videos, and articles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesLinkedIn profile URL or public slug (e.g. williamhgates)
pageNoPage number, 1-30 (default: 1). 20 posts per page — up to ~600 of the person's most recent posts.
get_sentimentNoAdd AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge.

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, 1-5 (default: 1). 20 posts per page — up to ~100 of the person's most recent posts."New value: +"Page number, 1-30 (default: 1). 20 posts per page — up to ~600 of the person's most recent posts."
  2. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the output contents (engagement metrics, author info, images, videos, articles) and implies a read-only operation with 'Get', but it does not mention pagination behavior, data limits beyond what the schema states, rate limits, or visibility caveats for private posts.

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?

The description is a single, front-loaded sentence that communicates the core action, target resource, input form, and key output categories without any filler. Every part is informative.

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?

For a tool with no output schema and no annotations, the description covers the returned data at a high level and includes core identification inputs. However, it lacks explicit usage boundaries, alternative tool routing, and behavioral caveats that would make it fully self-sufficient for an agent.

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 adds slight context by explaining the url parameter accepts a profile URL or public slug, but it does not meaningfully elaborate on page or get_sentiment beyond the schema's own descriptions.

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 description states a specific action ('Get recent posts'), a clear resource ('authored by a LinkedIn person'), and the input method ('profile URL or public slug'). It also differentiates from siblings by explicitly scoping to person posts rather than company posts or post details.

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 context is implied: you need a LinkedIn profile URL or public slug, and the target is a person rather than a company. However, there is no explicit guidance about when to choose this tool over alternatives like linkedin_company_posts or search_linkedin, and no exclusions are stated.

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.

Resources