google_searchGoogle keyword search: return web search results (link, title, snippet) for a keyword. 특정 키워드의 구글 검색 결과(링크·제목·요약)를 조회합니다. page 로 결과 페이지를 넘겨 가며 조회할 수 있습니다. [호출당 5포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 검색 결과 조회 페이지 (기본값 1) | |
| keyword | Yes | 검색할 키워드 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, and the description adds important context: cost per call (5 points) and pagination behavior. This goes beyond the structured metadata, though it does not cover potential rate limits or result caps.
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, with no wasted words. It states purpose, output fields, pagination, and cost in two short sentences, making it easy to scan.
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 parameters and no output schema, the description adequately covers what it returns (link, title, snippet), how to paginate, and cost. It does not elaborate on response structure, but that is not required given the simplicity and inherent web search 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?
The schema provides 100% coverage for both parameters. The description adds behavioral meaning to the 'page' parameter by explaining it allows paging through result pages, which is valuable beyond the schema's basic description.
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 explicitly states it performs a 'Google keyword search' and returns web search results (link, title, snippet). The verb 'search' and resource 'Google' are specific, and it distinguishes itself from sibling tools like google_image_search and google_lens_search by focusing on keyword-based web results.
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 clearly implies usage for keyword-based web search, mentioning pagination via 'page'. However, it does not explicitly name alternative tools or provide when-not-to-use guidance, so it falls slightly short of 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.
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.