linkedin_adLibrary_ad_getGet a LinkedIn Ad Library ad by ad id or Ad Library URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Public LinkedIn Ad Library URL for the ad. | |
| adId | No | LinkedIn Ad Library id for the ad. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds the useful fact that you can fetch by either ID or URL, which is not fully captured by the schema alone. However, it does not disclose details about response format, error handling, or rate limits. The annotations already declare readOnlyHint and openWorldHint, so the description's contribution is modest but non-contradictory.
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, concise sentence that front-loads the verb and resource. There is no extraneous information, and every word adds value.
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?
For a simple get tool with two optional parameters and no output schema, the description is adequate but not complete. It lacks mention of error conditions (e.g., invalid ID), response content, or how to discover ads via search. Given the sibling tools include a dedicated search, referencing that alternative would improve completeness.
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 coverage is 100% with both parameters described, so the baseline is 3. The description adds the relationship between the two parameters by stating 'by ad id or Ad Library URL,' clarifying that they are alternative identifiers. This goes beyond the schema, which lists them as independent optional fields.
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 states the verb (Get), the resource (a LinkedIn Ad Library ad), and the two ways to identify it (ad id or Ad Library URL). This leaves no ambiguity about what the tool does and differentiates it from the related search tool, linkedin_adLibrary_ads_search_list, which returns lists.
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 implies usage when you already have an ad ID or URL, but it does not explicitly state when to use this tool vs. alternatives such as linkedin_adLibrary_ads_search_list. There is no mention of search for discovery or exclusions, so the guidance is only implicit.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.