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

Since no annotations are provided, the description carries full responsibility for behavioral disclosure. It names the model (MiMo-V2.5), the scope of analysis, and states results are for direct use. However, it does not specify the output format or limitations, which keeps it from a 5.

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 description is front-loaded with its purpose and then organizes when-to-use cases in a clear paragraph. It is somewhat long but every section earns its place; the parameter summary is slightly redundant but not wasteful. Efficiency is good, though not as tight as a two-sentence high example.

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 2-parameter tool with no output schema, the description is adequately complete: it explains what it does, when to use it, and the parameters are fully covered by the schema. The only notable gap is the lack of output format information, but overall it does not leave major ambiguities.

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%, with both image_path and prompt already having detailed descriptions in the schema. The description's parameter paragraph merely restates what the schema offers (image_path required, prompt optional) and adds no new semantic meaning, so it remains at the baseline for high schema coverage.

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 states it uses the MiMo-V2.5 vision model to analyze a local image and returns layout, typography, spacing, color, and visual issue analysis. This gives a specific verb and resource, but does not explicitly distinguish it from sibling tools like compare_images or inspect_ui.

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 includes a dedicated '主动调用时机' (proactive invocation timing) section listing concrete triggers such as '用户要求看看/分析这张图' or when the user provides a screenshot path. This provides clear when-to-use guidance, but it does not mention alternatives or when not to use this tool, missing the full 5.

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

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

With no annotations, the description carries the full burden. It discloses that the model compares images directly across images (not separately analyzing then stitching) and details the returned information, including visual hierarchy changes. It doesn't mention limitations or failure modes, but core behavior is well covered.

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 description is moderately sized but front-loaded with the main action, followed by return details, use cases, and parameters. Every sentence adds value, though it could be slightly more concise by merging repetitive parameter mentions.

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 multi-image comparison tool with no output schema, the description adequately covers purpose, timing, parameters, and return categories. It lacks explicit output formatting or failure details, but is sufficient for an agent to select and invoke 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?

The schema covers 100% of parameters, including image_paths order semantics, path formats, and optional prompt. The description reiterates order semantics but adds no new information beyond the schema, 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 clearly states the tool compares 2-8 local images using a vision model, with a specific list of output categories (summary, differences, layout changes, etc.). It distinguishes itself from sibling tools like analyze_image and inspect_ui by emphasizing cross-image comparison rather than single-image analysis.

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 provides explicit invocation timing under '主动调用时机', listing scenarios such as design-to-implementation comparison, visual regression, and multi-scheme evaluation. However, it does not explicitly name sibling tools as alternatives or state when not to use it, so it lacks full exclusion guidance.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It reveals the model used (MiMo-V2.5), that the output is structured design data rather than literary prose, the exact 13 result categories, and the timing for proactive invocation. It stops short of stating return format details (e.g., whether output is JSON or a document) or any side effects/auth needs, but given that the tool is a vision analysis over local image paths, the disclosed behavior is adequately transparent for an AI agent.

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 description is substantial but well-structured: it leads with the model and purpose, then a compact list of the 13 return items, then a clear '主动调用时机' paragraph, and finally parameter guidance. Each sentence earns its place, though the 13-item enumeration is somewhat long. Front-loading is effective: the first sentence immediately tells the agent what the tool does and for whom.

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 no annotations and no output schema, the description does a solid job: it explains the output structure (13 named items), gives concrete trigger phrases, and clarifies parameter roles. It doesn't specify the exact output format (JSON vs. text) or how errors are handled, which are minor gaps. Given the tool's moderate complexity (mutually exclusive parameters, optional context, multi-image support), the description is sufficiently complete for an agent to invoke it correctly in most scenarios.

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 structured schema already documents all four parameters thoroughly, including file path examples and supported formats. The description adds value beyond the schema by explaining the conceptual roles of the parameters: '多图时逐个说明角色与差异' for image_paths, 'task 说明目标' with examples, and 'project_context 可选，提供技术栈等背景'. This semantic framing helps the agent map user intent to the correct parameter values.

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 opens with a specific verb ('用 MiMo-V2.5 视觉模型对 UI 截图做工程化分析') and clearly identifies the resource (UI screenshots) and purpose (replicating/modifying frontend for Coding Agents). It distinguishes itself from generic image analysis by emphasizing structured design data, listing the 13 return items, and stating when to proactively call it (e.g., user asks to '复刻这个页面' or '按截图/设计图改前端'). This separates it from siblings like analyze_image or compare_images.

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 includes a dedicated '主动调用时机' section that explicitly states when to use this tool ('用户要复刻这个页面', '按截图/设计图改前端', '根据参考图实现 UI'). It also clarifies that task describes the goal and project_context is optional, and the image_path/image_paths mutual exclusivity guides selection. Although it does not name sibling alternatives explicitly to exclude, the trigger conditions are concrete and distinct enough to prevent confusion with analyze_image/compare_images.

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