crawl_youtubeCollect a YouTube channel profile and its latest uploaded videos. 유튜브 계정(채널) 정보와 최근 게시한 동영상 정보를 수집해 반환합니다. [호출당 40포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | 수집할 유튜브 사용자(채널) 아이디 (예: CNN) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the read-only nature is covered. The description adds the cost (40 points per call) and confirms it returns profile and video info, but does not disclose behaviors like response limits, error handling, or whether it performs actual scraping. With annotations present, this is acceptable 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, with a front-loaded English sentence followed by a Korean translation and a cost note. It is efficient but slightly redundant due to the bilingual repetition; still, all information is relevant and no fluff is present.
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 low-complexity tool with one parameter and no output schema, the description adequately states what is returned (channel profile and latest videos). It could be more complete by specifying the number of videos or response format, but the cost note and annotation hints provide reasonable context.
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%, and the only parameter (user_id) is already clearly described in the schema with an example ('CNN'). The tool description does not add further parameter-level meaning, so the baseline of 3 applies.
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 collects a YouTube channel profile and its latest uploaded videos. It uses a specific verb ('Collect') and resource, and naturally distinguishes itself from sibling tools like download_youtube_video, which focuses on downloading a video rather than gathering channel-level information.
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: use this tool when you need a YouTube channel's profile and recent uploads. However, it does not explicitly mention when not to use it or name alternatives, so guidance is implied rather than explicit.
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.
Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.
Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.
80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.
Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.