image_similarityCompare a base image with another image and return a similarity score. 기준 이미지와 비교 대상 이미지의 유사도를 분석해 점수를 반환합니다. 원본 검증, 중복 이미지 탐지 등에 사용합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/jpeg, image/png, image/webp, image/bmp) (최대 50MB) | |
| compare_image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/jpeg, image/png, image/webp, image/bmp) (최대 50MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the tool is known to be non-destructive. The description adds the cost note '[호출당 10포인트]' (10 points per call), which is useful operational context. It does not describe the similarity score's range or interpretation, nor any error behavior, leaving behavior only partially transparent.
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: two sentences, one in English and one in Korean, plus a cost tag. It leads with the core action, then adds use cases, and finishes with the cost. No filler or redundant information, though the bilingual structure might slightly reduce skimmability.
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 two-parameter read-only tool with full schema coverage, the description covers purpose, use cases, and cost. It lacks explicit mention of the score output format (e.g., 0-1 scale) and does not state any limits or exceptions, but these are not critical for basic invocation. Overall it is complete enough for an agent to use 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?
Both parameters are fully documented in the input schema with format and size constraints, so the schema carries 100% of the parameter meaning. The description adds no additional parameter-level detail beyond referring to 'base' and 'comparison' images, which is already implied by the parameter names. Baseline 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 opens with 'Compare a base image with another image and return a similarity score', which names a specific verb, resource, and result. It also lists use cases (original verification, duplicate detection) and the cost per call. This clearly distinguishes it from the sibling url_similarity, which compares URLs rather than images.
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 states it is used for '원본 검증, 중복 이미지 탐지 등에 사용합니다' (original verification, duplicate detection, etc.), giving a clear context for when to invoke it. However, it does not explicitly mention when not to use it or point to any alternative for image comparison tasks, so it leaves some routing to inference.
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.