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이미지 대량 작업 상태

image_batch_status
Read-only

대량 이미지 작업의 진행 상태, 선차감·환급·현재 차감 포인트를 조회합니다. [무료]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes작업 ID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds useful behavioral context beyond that: it surfaces billing-related state (pre-deduction, refund, current deduction points) and marks the operation as [무료]. This helps the agent understand what kind of read operation it is and what domain information is involved, with no contradiction to 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that names the resource, the action, and the key returned information. The [무료] note is compact and informative, and there is no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one simple parameter, readOnly annotations, and no output schema, the description reasonably covers what the tool returns by listing progress status and point-related fields. It does not enumerate possible status values or error behavior, but for a 1-parameter status query this is a minor gap rather than a significant omission.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage for the single required parameter job_id, including its format pattern and description, so the description does not need to compensate. The description adds no extra semantic detail about job_id itself, which is appropriate given the high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb (조회합니다, 'queries') and a precise resource: the status of batch image jobs, including progress and point-deduction details like 선차감, 환급, and current deducted points. This clearly differentiates it from sibling tools such as image_batch_create and image_batch_result, even without naming them explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance or mention of alternatives. The intended use—checking the status of a batch image job by job_id—is implied by the description, but the description does not tell the agent when to prefer this over image_batch_result or when it is not appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.