base64_to_imageDecode a base64-encoded image string back into an image file. base64 로 인코딩된 이미지 문자열을 원본 이미지 파일로 디코딩해 반환합니다. "data:image/타입;base64," 접두어가 붙은 문자열도 허용됩니다. [호출당 2포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| base64 | Yes | 이미지 base64 문자열 (data:image/타입;base64, 접두어 허용) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, and the description adds that strings with a data URI prefix are allowed and that it returns an image file. The prefix allowance is already present in the schema parameter description, so the added value is minimal. The pricing note ([2 points per call]) is cost information, not behavioral transparency. No contradictions with annotations.
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 two sentences (in English and Korean) plus a brief cost note. It is front-loaded with the core action and includes necessary details without fluff. Each sentence earns its place, though the bilingual repetition is slightly redundant but acceptable.
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?
The tool is simple (one input, no nested objects, no output schema), and the description adequately states the conversion and input flexibility. However, it does not explain the output format or how the image file will be returned, which could be useful for an agent. Given the low complexity, the description is mostly complete.
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% for the single parameter, and the schema already documents that the string may include the data URI prefix. The description repeats this detail without adding extra meaning beyond the schema. 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 clearly states the tool's function with a specific verb ('Decode') and resource ('base64-encoded image string'), and the result ('an image file'). This distinguishes it from sibling tools, which handle other image operations but none specifically decode base64 to an image.
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 intended use is implied by the description: use this tool when you have a base64-encoded image string and need the original image file. However, there is no explicit 'when to use' vs. alternatives, nor any exclusions or conditions. The uniqueness among siblings makes it fairly clear, but guidance is not 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.