google_image_searchGoogle image search by keyword: return image results (image URL, source link, title). 특정 키워드의 구글 이미지 검색 결과(이미지 URL·출처 링크·제목)를 조회합니다. page 로 결과 페이지를 넘겨 가며 조회할 수 있습니다. [호출당 20포인트]
| 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 provide readOnlyHint and openWorldHint. The description adds useful behavioral context: it defines the return fields (image URL, source link, title), explains pagination via the 'page' parameter, and notes the 20-point cost per call. It does not contradict the annotations and enriches the agent's understanding of expected results.
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 short and front-loaded with the core purpose. However, it repeats the same information in English and Korean, which is redundant for an AI agent. Still, the pagination and cost details are efficiently conveyed, and there is no filler beyond the bilingual duplication.
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?
With no output schema, the description appropriately explains the return values (image URL, source link, title) and pagination mechanism. It lacks specifics like result count per page or the exact format of the page parameter, but for a straightforward image search tool, the provided information is adequate for an agent to invoke it 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 coverage is 100% — both 'keyword' and 'page' are described in the input schema. The description's mention of pagination adds functional nuance but does not go beyond what the schema already states (page is a search result page with default 1). No additional parameter clarification is needed.
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 searches Google Images by keyword and returns image URL, source link, and title. This clearly distinguishes it from sibling tools like google_search (web results) and google_lens_search (image-based search). The verb 'search' and resource 'Google image' are specific and unambiguous.
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 for keyword-based image search but provides no explicit when-to-use guidance, exclusions, or mention of alternatives. The pagination hint is the only contextual clue. Without stated alternatives, the agent must infer when this tool is appropriate relative to siblings.
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.