이미지 대량 작업 결과
image_batch_result완료된 대량 작업 결과 중 지정한 한 장을 이미지 콘텐츠로 반환합니다. [무료]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | 0부터 시작하는 이미지 번호 | |
| job_id | Yes | 작업 ID |
image_batch_result완료된 대량 작업 결과 중 지정한 한 장을 이미지 콘텐츠로 반환합니다. [무료]
| Name | Required | Description | Default |
|---|---|---|---|
| index | Yes | 0부터 시작하는 이미지 번호 | |
| job_id | Yes | 작업 ID |
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?
Annotations already provide readOnlyHint=true; the description adds the key behavioral constraint that only completed jobs are valid and that the output is image content. It does not contradict annotations, though it remains silent on error behavior for invalid indexes or incomplete jobs.
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?
A single, front-loaded sentence that conveys the action and condition without unnecessary words. The '[무료]' note is extra but minimal and does not detract from clarity.
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 simple read-only tool with fully described parameters and a clearly stated return type (image content), the description is sufficiently complete. It does not detail edge cases like index-out-of-range, but that is already constrained by schema maximum/minimum and does not impede correct invocation.
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 have schema descriptions (job_id as '작업 ID' and index with 0-based semantics and min/max), giving 100% schema coverage. The description adds no parameter-specific detail beyond what the schema already documents, so the 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 uses a specific verb ('반환합니다') and a precise resource ('완료된 대량 작업 결과 중 지정한 한 장'), clearly indicating it retrieves one image from completed batch results. This differentiates it from creation or status siblings, making its purpose unmistakable.
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 implies usage when a batch job is completed ('완료된'), but it does not explicitly state when to use this tool over alternatives like image_batch_status or image_batch_create. No direct exclusions or alternative referrals are provided.
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 targets a distinct operation: single image generation, single image editing, batch lifecycle, LLM chat, model listing, and text processing. The batch tools are clearly separated from single-image tools by their async/batch scope.
Tool names follow a predictable snake_case pattern with domain prefixes like image_, llm_, and text_. Most names are object+verb, but image_batch_result, image_batch_status, and llm_models use noun suffixes instead, which is a minor deviation.
With 9 tools, the server is well-scoped for an AI API covering image generation/editing, batch image operations, LLM chat, and text utilities. Each tool has a clear purpose and none feel redundant.
Core workflows are covered: single and batch image generation/editing, status/result retrieval, LLM model discovery and chat, plus text polishing and summarization. Minor gaps exist, such as no batch result listing endpoint and no way to retry or cancel individual images, but agents can work around these.