image_edit원본 이미지와 편집 지시로 이미지 한 장을 편집하고 이미지 콘텐츠로 반환합니다. 장당 25포인트입니다. [이미지당 25포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | 표준 출력 크기, 기본 1024x1024 | |
| prompt | Yes | 편집 지시, 최대 28,000자 | |
| image_url | Yes | 원본 이미지 — 다운로드 가능한 https URL (허용 형식: image/png, image/jpeg, image/webp) (최대 50MB) | |
| background | No | 배경 방식 | |
| output_format | No | 출력 포맷 | |
| idempotency_key | No | 같은 요청의 재전송으로 인한 중복 생성·과금을 막는 고유 키 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, idempotentHint=true, etc.), the description adds the behavioral detail that the result is returned as image content and that each use costs 25 points. This pricing disclosure is useful context that annotations do not provide.
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 first sentence is efficient and front-loaded, but the pricing information is duplicated: '장당 25포인트입니다' followed by '[이미지당 25포인트]' is redundant and does not earn its place.
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 6 parameters and no output schema, the description covers the core behavior, required inputs, return type, and cost. Optional parameters are documented in the schema, so nothing critical is missing for calling the tool 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%, so the schema fully documents all parameters. The description adds no parameter-level meaning beyond what is already in the schema, so the baseline of 3 applies.
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 identifies the action ('이미지 한 장을 편집' - edit one image), the required input (original image + edit instruction), and the return type (image content). The phrase '한 장' (one image) distinguishes it from batch siblings like image_batch_create, and '편집' (edit) distinguishes it from image_generate.
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 the clear usage context: use when you have an original image and an editing instruction. It does not explicitly name alternatives or exclusions, but the input requirement is specific enough to guide tool selection.
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.
Multiple tools target the same documents with unclear boundaries: identi_card_image1, ocr_identi1, and identity_document_id_card all accept a resident registration card image but differ in verification vs extraction vs masking, which agents will struggle to distinguish. The identi_card1-5 vs identi_card_image1-5 vs ocr_identi1-5 clusters are especially confusing, despite some helpful descriptions.
Naming is highly inconsistent: some tools use verb_noun (check_phone_valid, search_juso, pdf_merge), some are bare nouns (whois, nslookup, ocr, stt), and identity tools use three parallel schemes (identi_card1, identi_card_image1, ocr_identi1, identity_document_id_card) with no clear pattern. No single convention is followed across the toolset.
88 tools is an extreme count for a single MCP server, spanning identity verification, parcel tracking, LLM chat, image generation, PDF conversion, TTS, and network lookups. This is an API marketplace dump rather than a focused toolset, and the number far exceeds any reasonable scope.
The server's scope is unbounded, so it has gaps everywhere: no video transcription shortcut, no PDF splitting, no batch cancellation for images (only TTS), and identity clusters lack clear differentiation between verify/extract/mask workflows. Some subdomains like TTS have a full lifecycle, but overall the coverage is scattered and incomplete for the implied breadth.