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 provide readOnlyHint=true, indicating no side effects, and the description adds valuable behavioral context: it explicitly states the output is a JPEG image, lists supported input formats, and mentions a cost of 20 points per call. This goes beyond the annotations by clarifying the return format and resource cost, which an agent needs to know before invoking the tool.
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, with two sentences and a short cost note. It front-loads the core function in English, then adds the Korean translation and supplementary details (format support, cost) without any redundant or filler content. Every sentence contributes useful information.
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 tool with two parameters and no output schema, the description covers the essential aspects: function, input constraints (formats, size), output format (JPEG), and cost. It does not describe edge cases or error handling, but such details are typically not required for a straightforward processing tool. The description is sufficiently complete for an agent to call 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 description coverage is 100%: both parameters (image_url and threshold) have detailed descriptions covering allowed formats, size limit, range, default, and meaning. The description adds no extra parameter semantics beyond what the schema already provides, so the baseline score 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 verb (detect and blur) and resource (faces in an image), and specifies the output is a JPEG image. It is unambiguous and distinct from all sibling tools, which are focused on conversions, watermarks, or audio, making the tool's purpose immediately clear without needing to inspect the schema.
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 does not explicitly state when to use this tool versus alternatives, nor does it name any sibling tools as alternatives. Usage context is implied (any time you need to obscure faces), but there is no explicit guidance on when not to use it or which sibling to choose instead, which would be helpful for an agent deciding between tools.
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.
Each tool has a clearly distinct purpose, including the watermark pair: draw_watermark_image is visible text while set_watermark embeds an invisible code. The TTS job lifecycle tools are also cleanly separated by action and output type.
The set mixes conventions: conversion tools use input_to_output, watermark tools use verb_noun, TTS jobs use a tts_jobs_ prefix, and stt is a bare acronym. The names are readable but do not follow one predictable pattern.
At 19 tools, the server sits in the borderline 16-25 range and spans document conversion, image processing, watermarking, audio/video, and async TTS. Most tools earn their place, but the overall surface feels somewhat heavy for a single conversion-focused server.
The server covers its core domains well: document conversions, watermarking with both visible and invisible methods, PDF operations, and a full async TTS workflow. Minor gaps exist, such as missing image-to-PDF or Excel-to-JSON inverse conversions, but agents can generally complete workflows without dead ends.