facebook_events_search_listSearch Facebook events by name. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query text for Facebook events by name. | |
| cursor | No | Opaque pagination cursor returned by a previous response. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds the valuable behavioral detail that the tool returns a list and supports pagination via cursor. This goes beyond the annotations by explaining the return shape and how to navigate pages.
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 compact and front-loaded: it states the core purpose in the first clause and adds the key return/pagination detail in the second sentence. No filler or redundancy. Every word earns its place.
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 search tool with a single required parameter and optional cursor, the description covers the essential information: what is searched (by name) and how pagination works. Given the absence of an output schema, mentioning that it returns a list is helpful, though it could hint at the structure of the list items. Overall, adequate for an agent to use correctly.
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 'query' and 'cursor' are described explicitly in the input schema. The description adds little beyond the schema—it echoes the 'by name' concept and the pagination hint. This is a case where the schema does the heavy lifting, so a baseline of 3 is appropriate.
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 states a specific action ('Search Facebook events by name') and clearly indicates the resource and scope. It differentiates from siblings like facebook_event_get (singular) and facebook_events_list (likely a non-search list) by the explicit 'by name' qualifier and the 'Returns a list' note.
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?
No explicit guidance is given on when to use this tool versus alternatives. It does not mention when to prefer this over facebook_events_list, facebook_event_get, or facebook_profile_events_list. The usage context is only implied by the name and 'by name' phrasing, which is insufficient for an agent to make routing decisions without inspecting other tools.
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.