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by io.github.CDCStream

linkedin_ad_library_search_ads

LinkedIn publishes run dates, impressions and targeting only on a subset of creatives. Search is the SERP card; source is native|extended. Costs 2 credits. Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAdvertiser / account owner name (min 2 when used). Provide q/company, keyword, or companyId.
cacheNoSet true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh.
limitNoMax items to return (default 20, max 200). Flat 2 credits per call on the native path; the extended fallback bills ~3.5 credits per returned ad. Response `source` is native or extended so you can see which price applied.
cursorNoPagination token from paginationToken / nextCursor.
companyNoAlias of q — advertiser / account owner name.
countryNoSingle ISO country code. Default US. Ignored when countries is set.
endDateNoCustom range end YYYY-MM-DD (use with startDate).
keywordNoOptional keyword filter on ad creative copy.
companyIdNoLinkedIn numeric company id for exact advertiser match.
countriesNoComma-separated ISO country codes (e.g. US,CA,MX).
startDateNoCustom range start YYYY-MM-DD (use with endDate).
paginationTokenNoAlias of cursor.

TDQS

B3.1/5.0
Behavior4/5

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

With no annotations, the description carries the disclosure burden and does meaningful work: it reveals that run dates/impressions/targeting appear only on a subset of creatives, that results come from native or extended sources, that empties/failures aren't billed, and that cache=true gives a free 24h hit. The blanket 'Costs 2 credits' slightly oversimplifies the schema's extended-fallback ~3.5 credit billing, but overall it is transparent and useful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four dense sentences with no filler, and it front-loads the most important caveat about data availability. The 'SERP card' phrasing is slightly jargon-heavy, but the text is compact and each clause adds operational value.

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 12-parameter search tool with no output schema and no annotations, the description covers pricing, caching, and data limitations but never states what the response contains beyond 'SERP card'. It also omits any pointer to when this should be used instead of linkedin_ad_library_ad_details, so an agent is left without the full decision context.

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 coverage is 100%, and every parameter already has a substantive description including defaults, aliases, and constraints. The tool description adds no new parameter meaning beyond repeating cache=true; therefore a baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description does not explicitly say 'searches LinkedIn Ad Library ads'; it opens with a data caveat and defines 'Search is the SERP card'. The tool name and schema make the intent clear, but the description itself is vague about the verb+resource and does not distinguish itself from linkedin_ad_library_ad_details.

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

Usage Guidelines2/5

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

It provides operational guidance (cache behavior, credit cost, no charge for failures) but no when-to-use or when-not-to-use guidance relative to sibling ad-library tools. There is no mention of alternatives such as linkedin_ad_library_ad_details or the conditions that would select this search tool.

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

B3.2/5.0
Disambiguation3/5

The platform-prefix convention keeps most of the 178 tools clearly separated, but several clusters are genuinely ambiguous: tiktok_live_info is explicitly described as 'Identical to TikTok Live', instagram_basic_profile and instagram_channel_details both return profile stats, and facebook_profile_posts overlaps with facebook_profile_reels. The generic 'Summarizer' descriptions for facebook_summarize, instagram_summarize, and tiktok_summarize provide no disambiguating detail at all.

Naming Consistency4/5

The dominant snake_case platform_resource_suffix pattern is followed remarkably consistently across 178 tools (e.g. youtube_channel_videos, tiktok_search_users, reddit_subreddit_posts). Minor deviations exist: the same creator resource is called 'channel' in some tools (tiktok_channel_details, instagram_channel_posts) but 'profile' or 'user' in others (facebook_profile_posts, twitch_user_videos, linnkme_profile); link-in-bio tools mostly use _page but linkme uses _profile; and the video_summarize/video_transcript pair lacks a platform prefix.

Tool Count2/5

At 178 tools this is far beyond what any agent can efficiently navigate in a single flat namespace, and even individual platform subsets exceed reasonable bounds (TikTok alone has ~34 tools, YouTube ~25). The sheer breadth of the multi-platform scope partially justifies the count, but the server would be far more usable split into per-platform servers.

Completeness4/5

The read-only data surface is impressively thorough: nearly every platform has profile + content + search + comments coverage, and TikTok, YouTube, Instagram, and Facebook are covered end-to-end including shops, ads, transcripts, and summaries. Notable gaps are minor: Twitter has no keyword search tool, LinkedIn lacks comments, and Reddit has no user-profile endpoint, but none of these create dead ends for the server's core data-retrieval purpose.

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