Get detailed information about a specific LinkedIn job listing by URL or numeric job ID. Returns title, company, location, posting date, job type, experience level, workplace type, industry, salary, benefits, and full description.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | LinkedIn job URL (e.g. https://linkedin.com/jobs/view/1234567890) or numeric job ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns a comprehensive set of fields (title, company, location, date, type, etc.), giving the agent a clear picture of the response behavior. The word 'Get' implies a read-only operation, though it does not explicitly state non-destructiveness or error conditions. Still, it provides substantial behavioral context.
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 a single, focused sentence that front-loads the action ('Get detailed information') followed by the resource and a concise list of returned fields. Every word adds value, with no redundant or verbose phrasing.
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?
This is a simple single-parameter read tool with no output schema and no annotations. The description fully covers what the tool does, how it is invoked (URL or ID), and what it returns (all major job attributes). For its complexity level, the description is complete and self-sufficient.
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%, with the url parameter already described as 'LinkedIn job URL ... or numeric job ID.' The tool description adds no new parameter semantics beyond repeating that the input can be a URL or numeric ID. This meets the baseline for parameters already well-documented in the schema, but does not elevate it.
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 uses a specific verb and resource: 'Get detailed information about a specific LinkedIn job listing.' It clearly differentiates from sibling tools like search_linkedin_jobs by focusing on a specific listing by URL or ID. It also enumerates the returned data fields, making the tool's purpose unambiguous.
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 description clearly implies when to use this tool: when you have a LinkedIn job URL or numeric job ID and need detailed information. It does not explicitly mention alternatives or exclusions, but the 'by URL or numeric job ID' qualifier provides clear context that this is for retrieving a single job's details, not for searching or listing jobs.
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.
