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

With no annotations provided, the description carries the full burden and delivers rich behavioral detail: concurrency strategy (5 for gpt-image-2, serial+1.5s gap for the openai line), failure isolation ('任意一张失败不影响其他张'), high-resolution auto-switching to serial processing, rate-limit risk disclosure, and note that cost/time scale linearly with batch size. This exceeds what any annotation set would typically provide.

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?

Well-structured with clear section headers ([WHAT], [WHEN TO USE], [并发策略], [LIMITS], Args, Returns, Examples) and front-loaded summary. Though long, every block earns its place — this is a complex 6-parameter batch tool with output-format documentation and routing logic; the length is proportionate to the complexity. No filler or redundant restatement.

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?

Given the tool's complexity, 0% schema coverage, and an output schema, the description covers everything an agent needs: input parameter semantics, return format (ok/total/succeeded/failed/concurrency/results), concurrency and failure behavior, limits, and a working example. The presence of an output schema relaxes the burden on return-value explanation, and the description still documents it. Complete.

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 0%, so the description must compensate, and it does so thoroughly. The Args section gives each parameter real meaning beyond the schema titles: prompt gets an example, size gets the 1K/2K/4K values and default, model gets the two accepted values, save_dir gets the filename pattern batch_<ts>_<idx>.png and the security-root constraint, api_key gets its MICU_API_KEY override behavior with base_url locking noted. Full compensation for the coverage gap.

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 opening line states a precise verb+resource+scope: 'N 张输入图 → N 张输出图，每张独立应用同一指令' (batch edit N images, each independently applying the same instruction). The [WHAT] section plainly states it calls image_edit per image and merges results. It differentiates itself from siblings by explicitly declaring '用多张图作风格参考画 1 张新图' is NOT this tool, and routes single images to image_edit. Unambiguous.

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 [WHEN TO USE] section gives explicit selection criteria: use this tool when the user has multiple images all needing the same modification (watermark, background, color grading), use image_edit for a single image, and explicitly states the multi-reference case is not implemented. It also adds concurrency strategy per model line. Nothing is left to inference.

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 exist, so the description carries the full burden, and it delivers: discloses the underlying /v1/images/edits endpoint, the automatic 2K/4K switch to gpt-image-2-openai with cross-process lock serialization, the removal of the old 4K hard-block, mask alpha semantics (alpha=0 = edit region, alpha=255 = preserve), actual_size verification, and explicit error conditions (HTTP 524 queue busy, missing image_path). No contradiction with annotations since none are provided.

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 long, but every section earns its place and the header-based structure ([WHAT], [WHEN TO USE], [尺寸能力], [MASK 工作原理], Args, Returns, Examples, Common errors) makes it highly scannable with the purpose front-loaded. Minor deduction for the dated 尺寸能力 and 路由实现 sections, which are somewhat redundant with the args and could be trimmed; overall this is efficient organization for a high-complexity tool, not padding.

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 8 parameters, 0% schema coverage, no annotations, and moderate routing complexity, the description is complete: all parameters defined with valid values, usage context with sibling routing, behavioral specifics, a full Returns dict specification, concrete examples for each use case (background swap, mask edit, 2K upscale, 4K refine), and common errors with remediation. Despite the output schema existing, the description also documents the return structure — a bonus that exceeds the baseline requirement.

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 0%, so the description must fully compensate — and it does. Every one of the 8 parameters is documented beyond the schema: prompt includes a concrete example and advice (the more specific the better); image_path lists supported formats (PNG/JPG/WebP); size enumerates exact valid values per tier with the 16-multiple constraint; model documents auto-switch behavior; save_dir notes the safety-root restriction; basename specifies the character whitelist; api_key explains the base_url lock. This is exemplary compensation for a zero-coverage schema.

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?

Description states a specific verb (edit), resource (single input image), and mutation type (modify/replace/add/remove parts). Clearly names and excludes siblings: image_generate (from scratch), image_batch_edit (batch), image_multi_reference (multi-style-reference). An agent can unambiguously route to this tool based on the WHAT and WHEN TO USE sections alone.

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 [WHEN TO USE] section provides crisp selection criteria with explicit alternatives and exclusion conditions: use this when 1 image + edit intent; switch to image_generate if no image; to image_batch_edit for batch ops; to image_multi_reference for style-reference synthesis. Every branch names the sibling and the condition that selects it — nothing is left to inference.

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 provided, the description carries the full burden – and it excels. It discloses automatic size inference from prompt keywords, fallback to 1024x1024, auto model selection based on max edge ≥1600, forced N=1 for ≥2K, 5-way concurrency for 1K with N>1, security constraints (save_dir must be under MICU_SAVE_DIR_ROOT, basename whitelist, base_url locked at startup to prevent key leakage), and measured deviations from requested sizes on certain models. It even documents retry behavior for timeouts. This is far beyond typical transparency.

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 long but extremely well-structured with clear section headers ([WHAT], [WHEN TO USE], [SIZE], [PROMPT]) and bullet points. The most critical info (what it does, when to use) is front-loaded. Examples and common errors are placed at the end where they belong. While it's verbose, every section adds distinct value – no redundant filler. The length is justified by the tool's complexity (8 params, routing logic, safety checks). It earns a 4, not 5, only because it is genuinely long and might overwhelm a quick scan.

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?

Despite having an output schema (returns dict), the description still explains each return field (ok, model, size, requested_n, saved, errors, notes) and their semantics, which is helpful. It covers the full decision tree (size selection, model routing, concurrency), all constraints (16-pixel multiples, aspect ratio limits, pixel bounds), and common error scenarios with remediation. It also includes practical examples and edge-case notes (like 1920×1080 → 2048×1152). For a tool with this many parameters and automatic behaviors, the description is remarkably complete.

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 0% (all 8 parameters lack schema descriptions), so the description must provide full parameter semantics – and it does. Each parameter gets its own line with default, constraints, and examples: prompt with character limit and example, size with valid formats and auto-inference logic, n with range and concurrency implications, model with optional values and selection rule, quality with allowed values, save_dir with security path restriction, basename with regex whitelist, and api_key with env override note. The description fully compensates for the schema gap.

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 clear verb and resource: '文本生成图像（text-to-image）' – it generates images from text prompts and saves them to disk. It directly distinguishes itself from siblings by stating that image_edit is for editing with a reference image and image_multi_reference is for style transfer from multiple references. An agent can unambiguously route to this tool versus alternatives.

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 [WHEN TO USE] section explicitly states when to use this tool ('画/生成/创建一张图' without reference), names two sibling alternatives with exact conditions (image_edit for single ref, image_multi_reference for multiple refs), and points to server_info for size selection when uncertain. It also gives negative guidance (use other tools when refs are provided), making the routing decision completely deterministic.

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 full burden. It discloses the exact endpoint (/v1/images/edits), the deprecated endpoint that is silently ignored, automatic high-quality line switching for ≥2K, retry behavior for intermittent rejections, error propagation, and concrete size limits (2-10 images, ≤8MB total). It even explains the token linearity (560×N). This is exceptionally transparent about runtime behavior.

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?

Though long, every section earns its place: [WHAT] states the core purpose, [WHEN TO USE] gives routing, [当前线路] covers current routing, [路由实现] explains implementation details, [LIMITS] lists constraints, and parameter descriptions are structured. The most critical info (purpose and routing) is front-loaded, and the rest is organized with clear headers, making it scannable despite length.

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?

Given the tool's complexity (7 parameters, high-resolution handling, routing logic, limits), the description is complete. It covers input constraints, output verification, error handling, and alternatives. It even provides three examples demonstrating 1K, 2K, and 4K use cases. Nothing an agent needs to correctly select and invoke this tool is missing.

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 0%, so the description must compensate, and it does comprehensively. It explains every parameter: prompt with an example, image_paths with count constraints, size with 1K/2K/4K semantics and default, model with the high-quality line mapping, save_dir safety restriction, basename pattern rule and default, and api_key override behavior. It also documents the return dict fields. This is far beyond minimal compensation.

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 '多图融合参考 → 输出 1 张新图' (multi-image fusion reference → output one new image), explicitly stating the verb (fuse/combine), resource (multiple reference images + prompt), and output (one new image). It then contrasts with image_batch_edit (N-in-N-out vs N-in-1-out), clearly differentiating it from its sibling. This is a precise, distinguishing definition.

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?

A dedicated [WHEN TO USE] section lists concrete user requests and maps each to this tool or its alternatives (image_batch_edit for batch edits, image_edit for single image, image_generate when no references). It also includes explicit 'if...use...' conditions and a 'Common errors' section with troubleshooting guidance. This provides unambiguous routing for an agent.

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 provided, the description carries the full burden. It indicates the tool returns information but does not disclose whether it is a read-only operation, any authentication requirements, or side effects. While it is likely a safe read, the lack of explicit disclosure and absence of annotations leaves this gap.

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 a single sentence that directly states the core information returned. Zero waste, perfectly front-loaded, and appropriately sized for a parameterless tool.

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?

The description lists the key content areas (model, parameter constraints, routing, security boundaries), which is sufficient for a simple info tool. An output schema exists to document the return structure, so the description does not need to explain return values. The only gap is lack of explicit usage context, but given the simplicity, it is nearly complete.

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 tool has zero parameters, so the schema provides no parameter documentation. The description does not need to add parameter meaning since there are none. Baseline 4 applies as the description is not compromised by missing parameter details.

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 states a specific verb (返回/return) and resource (当前 Image2 模型、参数约束、路由和安全边界), making the purpose clear. It is distinguishable from sibling tools that perform editing or generation actions, though it does not explicitly name alternatives. The resource is distinct enough that an agent can infer the purpose without opening the schema.

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?

No guidance is provided about when to use this tool versus the sibling action tools. The description only states what information is returned, but does not give context on when an agent should call it (e.g., before editing/generating to check constraints). Usage is implicitly obvious but not explicitly stated.

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