web_ask_runAsk a natural-language question about a specific web page and get an LLM-generated answer. Accepts a URL.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Natural-language question to answer about the page content. | |
| url | Yes | Web page URL to fetch. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and openWorldHint=true. The description adds that the answer is 'LLM-generated', which is meaningful behavioral context beyond what annotations convey about safety or external access. It does not contradict any annotation.
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 core purpose and then adds the output type. Every word contributes value; there is no fluff or repetition.
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 tool with two well-documented parameters, no output schema, and annotations covering safety, the description is sufficient. It tells the agent what the tool does, what inputs it takes, and what to expect as output. Minor gaps like handling of invalid URLs or non-text pages are edge cases not critical for basic invocation.
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%, with both parameters ('url' and 'q') having clear descriptions. The tool description adds no extra parameter semantics beyond the schema, so the 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 verb ('ask'), a specific resource ('a specific web page'), and the output ('an LLM-generated answer'). This clearly differentiates it from siblings like web_search_run (which searches the web) and web_extract_run (which extracts raw content). No ambiguity or tautology.
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 clear context: the tool answers natural-language questions about a single page. It implies when to use it (when you need a synthesized answer rather than raw content) but does not explicitly name alternatives or state when not to use it. It lacks the explicit sibling routing seen in top-tier examples.
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.