Skip to main content
Glama

API Direct

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 for pagination (default: 1)
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.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden. It discloses return fields (post content, engagement metrics, media, repost flag, optional AI sentiment), notes the need for at least one search criterion, and mentions that some filters apply "alongside a query." It does not mention rate limits or auth, but for a read-only search tool this is acceptable. The schema covers cost of sentiment, so no criticism.

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 compact: a clear opening sentence, a usage instruction, a list of key filters, and a summary of return fields. No wasted words, and it is well front-loaded with the core purpose. Each sentence contributes meaningful information.

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?

For an 11-parameter tool with no output schema, the description covers the essential usage context: it names the main filters, explains the query/filter requirement, and summarizes what is returned. It references a sibling tool for company IDs. It does not go into pagination details, but those are in the schema. Overall, sufficiently complete for an AI agent.

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 baseline is 3. The description adds semantic value by grouping filters into concepts (e.g., distinguishing "posts BY a person" vs "posts that MENTION a person"), clarifying that author_title and author_industry apply alongside a query, and giving examples for author_title. This goes beyond the schema's individual field 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 opens with a specific verb+resource: "Search LinkedIn posts." It clearly distinguishes this from sibling tools like search_linkedin_companies and search_linkedin_jobs by focusing on posts. It also enumerates the scope (filters, returns) making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description says "Provide a `query` and/or a filter below," indicating a required condition for usage. It also points to search_linkedin_companies for company IDs, providing a cross-tool reference. However, it does not explicitly state when to use this tool over other search tools, though the name and context make it reasonably clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All API Direct toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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