Get detailed information about a specific LinkedIn post by URL. Returns full post text, author details, and engagement metrics (likes, comments, shares, reaction breakdowns).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | LinkedIn post URL | |
| get_sentiment | No | Add AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of signaling safety. The verb 'Get' and the promise to 'Return' data clearly indicate a read-only operation. It also discloses what the user will receive (post text, author details, engagement metrics), giving a transparent view of the output. It does not mention limitations like missing public posts or rate limits, but for a read operation this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight sentences front-loaded with exactly what the tool does. Every phrase adds value—'specific', 'by URL', and the list of returned data—without any redundant or filler wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description does a good job explaining the return value and distinguishing itself from sibling tools. It lacks explicit mention of the optional get_sentiment behavior (though that is covered in the schema) and potential edge cases like invalid URLs, but overall it is sufficiently complete for a straightforward read tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%—both 'url' and 'get_sentiment' have clear descriptions in the schema. The tool description adds no new parameter-specific meaning beyond what the schema already provides, so the baseline 3 applies. The only slight addition is that 'by URL' reinforces the purpose of the url parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Get detailed information') and the exact resource ('a specific LinkedIn post by URL'). It also lists the key output content (full post text, author details, engagement metrics), which distinguishes it from sibling tools that search or list posts rather than fetch details for a single post.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'by URL' provides clear context for when to invoke this tool: whenever you have a direct LinkedIn post URL and want its details. It does not explicitly state alternatives or exclusions, but the scope is unmistakable given the 'specific LinkedIn post by URL' wording.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
