web_search_runSearch the public web and return ranked organic results with snippets. Accepts a query. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (1-based). Default: 1. | |
| query | Yes | Search query text to run against the public web. | |
| region | No | ISO 3166-1 country code for localized results (e.g. US, GB, CA). | |
| datePosted | No | Optional filter by when results were posted. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds that results are organic and that pagination uses a cursor, which is useful. However, it doesn't disclose rate limits, error behavior, or result count limits. Given annotations exist, this is adequate but not rich.
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 concise at two sentences and front-loaded with the core purpose. The second sentence 'Accepts a query' is redundant but not harmful. Overall, it's efficient with minimal waste.
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 four well-documented parameters and no output schema, the description covers the basics: what it does, returns a list, and pagination. However, the pagination instruction is vague and potentially misleading given the schema uses page numbers. It doesn't explain the output structure beyond 'snippets' or clarify what 'organic' means in practice. It's minimally complete.
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?
All four parameters have descriptions in the schema (100% coverage), so the description adds little beyond stating 'Accepts a query.' The mention of cursor pagination conflicts with the schema's 'page' parameter, which could confuse agents. Baseline 3 is appropriate because the schema carries most semantics, but the inconsistency slightly reduces 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 clearly states the tool searches the public web and returns ranked organic results with snippets. This distinguishes it from platform-specific search tools and from web_ask_run, web_crawl_run, etc. However, it doesn't explicitly name alternatives, so it's not a 5.
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 provides no guidance on when to use this tool versus alternatives like twitter_search_list or web_ask_run. It simply states what it does without context for selection. The only hint is 'public web' and 'organic results,' but no explicit when/when-not.
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.