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sweet8413-spec

TypeScript MCP Server Boilerplate

generate-image

Create images from text prompts using AI generation. Specify prompt details and adjust inference steps to control output quality.

Instructions

텍스트 프롬프트로 이미지를 생성합니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes이미지 생성 프롬프트
num_inference_stepsNo추론 스텝 수 (1~10, 기본값: 4)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic function ('generate an image') without mentioning any behavioral traits such as rate limits, authentication needs, output format, or potential side effects (e.g., resource usage, cost). For a generative tool with no annotation coverage, this leaves significant gaps in understanding how it behaves beyond the core action.

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, efficient sentence that directly states the tool's purpose without any extraneous information. It is appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place, contributing to clarity.

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

Completeness2/5

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

Given the complexity of an image generation tool, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., performance, limitations), output characteristics (e.g., image format, size), and usage context. The description alone is insufficient for an agent to fully understand how to invoke and interpret results from this tool.

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 description coverage is 100%, with both parameters ('prompt' and 'num_inference_steps') well-documented in the schema. The description adds no additional meaning beyond what the schema provides, as it doesn't explain parameter roles or interactions. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to.

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

Purpose4/5

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

The description clearly states the tool's purpose: '텍스트 프롬프트로 이미지를 생성합니다' translates to 'Generate an image from a text prompt.' This specifies the verb ('generate') and resource ('image'), making the function unambiguous. However, it doesn't distinguish from the sibling tool 'greet', which appears unrelated, so differentiation isn't necessary but could be noted for completeness.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It states what the tool does but doesn't mention any prerequisites, constraints, or scenarios where it should be preferred over other methods. With no explicit usage context, the agent must infer usage from the purpose alone.

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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