facebook_adLibrary_companies_searchSearch Facebook Ad Library companies / advertisers by name. Accepts a company name. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query text for Facebook ad library companies. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds a useful behavioral detail: it returns a list and mentions cursor-based pagination. This goes beyond what annotations provide, giving agents a concrete expectation about response structure and how to handle large result sets.
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 sentences, front-loaded with the purpose, and includes the pagination note without any fluff. Every sentence earns its place, and the critical details (what it does, acceptance criteria, pagination) are presented efficiently.
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 single-parameter search tool with readOnly and openWorld annotations, the description covers the core usage: search by company name and handle pagination. It does not explain how to use the cursor in detail, but that is likely a standard pattern. The absence of an output schema and the simplicity of the API makes this adequate, though a brief note about the return format's shape would push it to a 5.
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%, so the parameter itself is fully documented. The description adds semantic value by stating the query is a 'company name', which narrows the meaning beyond the generic 'Search query text'. This clarifies what kind of input is expected, exceeding the baseline.
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 a specific verb ('Search') and resource ('Facebook Ad Library companies / advertisers by name'), and notes it accepts a company name and returns a list. It is clear what the tool does, but it does not explicitly differentiate itself from sibling tools like facebook_adLibrary_ads_search_get or facebook_company_ads_list, so it falls short of a top score.
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 context: you use this when you have a company name and want to find the matching advertiser. It also mentions pagination handling. There is no explicit guidance on when not to use it or alternatives, so it gets a 4 rather than a 5.
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.