GPT Image Playground MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool handles a distinct phase of the image generation workflow: generating, checking status, and downloading. There is no overlap in their purposes, making it clear which tool to use at each step.
Naming Consistency5/5All three tool names follow a consistent verb_noun pattern (generate_image, get_task_status, download_image), using snake_case and clear action-first naming. The pattern is uniform across the set.
Tool Count5/5With only 3 tools, the server is tightly scoped to a single workflow (generate, track, download). This is appropriate for a focused purpose and each tool is necessary for the complete flow.
Completeness4/5The workflow covers the essential lifecycle: generation initiation, status polling, and downloading results. A minor gap is the lack of a cancellation or listing tool, but agents can work around this by waiting or using status checks.
Average 3.4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states the action (saving a file) without mentioning side effects, overwriting behavior, permission requirements, or error handling. The agent has no information about what happens if the task is incomplete or if the output path already exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no unnecessary words. It front-loads the key action and scope, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no output schema, no annotations), the description is too minimal. It lacks essential context for parameter usage, expected behavior on failure, and any safety considerations, making it incomplete for an agent to confidently invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not explain any of the three parameters (task_id, image_index, output_path). The agent must infer meaning solely from parameter names, which is insufficient, especially for optional image_index which could be ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: downloading an image from a completed task to a local absolute path. It uses a specific verb (save/download) and resource (image from completed task), distinguishing it from sibling tools like generate_image and get_task_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions the precondition that the task must be completed, giving clear context for when to use this tool. However, it does not mention alternatives or when not to use it, though the distinct purpose makes the use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- 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. It only states that the tool queries a status, implying a read-only operation, but it does not disclose return format, error behavior, or any Playground-specific constraints or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
One short sentence with no filler, directly stating the tool's purpose. It is front-loaded and appropriately sized for a simple status-query tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one parameter and no output schema, yet the description does not explain what the status response looks like or how it relates to the sibling generate/download workflow. This leaves the agent with gaps in knowing how to interpret the result or integrate the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the task_id parameter. While '某个生成任务' hints at the parameter's purpose, the description adds no concrete meaning beyond the schema's bare field name and type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb '查询' (query) and a clear resource '生成任务的状态' (status of a generation task), scoped to the Playground page. This distinguishes it from sibling tools that generate or download images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage context is implied by the nature of the tool—checking status after generation—but there is no explicit guidance on when to use it vs. alternatives, no exclusions, and no mention of prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
没有注解,描述承担了行为披露的全部责任。描述说明了请求会被串行处理、等待页面完成报告、没有20秒超时限制,这些是有用的行为细节,让代理了解执行模型。但未涉及权限、副作用或错误处理等,考虑到无注解,整体披露较充分。
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
描述简洁,两个句子传达了核心功能和行为特征,没有冗余信息。第二句补充了重要的执行细节(串行处理、无固定超时),结构合理,但可再稍微结构化一点。
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
工具接受2个参数,无输出schema,描述提供了行为的额外细节(串行处理、异步等待)。但对于兄弟工具(get_task_status、download_image)的关系没有提及,缺少集成上下文的说明。考虑到相对简单,描述基本够用,但可以更全面。
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
schema覆盖率为0%,描述仅提到了'注入本地参考图'对应reference_images参数,但核心参数prompt没有额外解释,仅从字段名推断其含义。描述未能完全补偿schema参数的缺乏,特别是prompt的格式、要求或约束未提及。
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
描述明确说明了工具的动作是生成图片,资源是GPT Image Playground页面,并提到可注入本地参考图。与兄弟工具get_task_status和download_image在功能上明显区分,目的清晰具体。
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
描述暗示了使用场景(需要通过已打开的页面生成图片),但没有明确说明何时应使用此工具而非其他兄弟工具,也未提及限制条件(如页面必须已打开、注入参考图的步骤等)。缺少明确的'何时使用/何时不使用'指导。
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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