face_blurDetect faces in an image and blur (mosaic) them. 이미지 파일에서 얼굴을 인식해 해당 영역을 모자이크 처리한 이미지(JPEG)를 반환합니다. PNG, JPEG 등 일반 이미지 포맷을 지원합니다. [호출당 20포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/png, image/jpeg, image/webp, image/bmp) (최대 50MB) | |
| threshold | No | 얼굴 추출 민감도 (0 ~ 0.9, 기본값 0.5, 작을수록 민감하게 추출) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint: true, which aligns with the description's statement that it returns a new processed image without modifying the source. The description adds useful behavioral context beyond annotations: it specifies the output format (JPEG), supported input formats (PNG, JPEG, etc.), and a cost of 20 points per call. This gives the agent a clearer picture of the tool's behavior, including pricing, which is not in the 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 concise and well-structured: the primary purpose is stated in the first sentence, followed by a Korean reiteration for clarity, then supported formats and cost. Every sentence adds value, and the content is front-loaded with the core action. It is not verbose or repetitive, making it easy for an agent to quickly grasp the tool's function.
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 relatively simple with only two parameters and no output schema. The description provides sufficient context: the main action, input requirements, and output format (JPEG). The schema covers parameter details, and annotations cover read-only behavior. The description might be slightly incomplete regarding error messages or the exact nature of the blur effect, but it is adequate for an agent to understand what to expect. Given the simplicity, a score of 4 reflects that it covers most necessary context without significant gaps.
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 description coverage is 100%, meaning both parameters (image_url and threshold) are already documented in the schema with details about allowed formats, size limits, and sensitivity ranges. The tool description adds minimal information about parameters beyond what the schema already provides ('PNG, JPEG 등 일반 이미지 포맷을 지원합니다' is redundant with the schema). Since the schema already carries the parameter semantics, a baseline score of 3 is appropriate; the description does not enrich parameter understanding.
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 states a specific verb ('Detect faces in an image and blur (mosaic) them') and a clear resource (an image), and it clarifies the output (JPEG image). It distinguishes itself from the sibling tool 'face_detection' by indicating that it not only detects but also blurs faces. The purpose is unambiguous and differentiates from alternatives.
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 explains the core functionality ('Detect faces in an image and blur them') and mentions supported formats and cost, but it does not explicitly state when to use this tool over alternatives like 'face_detection' nor when not to use it. The intended use case is implied but not explicitly contrasted with sibling tools. There is no guidance on exclusions or alternatives.
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.