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

With no annotations provided, the description carries full burden for behavioral disclosure. While '添加边框效果' implies a transformation operation, it doesn't specify whether this is destructive to the original image, what permissions might be needed, performance characteristics, or error conditions. The description provides minimal behavioral context beyond the basic operation.

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 extremely concise at just 7 Chinese characters ('为图片添加边框效果'), which directly states the tool's purpose without any wasted words. It's appropriately sized for a straightforward image processing operation and front-loads the core functionality.

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 moderate complexity (6 parameters, image transformation) and the presence of both comprehensive input schema (100% coverage) and output schema, the description is minimally adequate. However, for a mutation tool with no annotations, it should provide more context about behavioral implications and usage scenarios to be truly 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 description adds no parameter information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 6 parameters. The baseline score of 3 reflects adequate schema documentation, but the description doesn't provide additional context about parameter interactions, constraints, or usage patterns.

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 '为图片添加边框效果' clearly states the tool's purpose as adding border effects to images, which is a specific verb+resource combination. However, it doesn't explicitly differentiate this tool from its many image processing siblings like 'add_shadow' or 'add_watermark', which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. With numerous sibling tools for image effects (add_shadow, apply_vignette, etc.), there's no indication of when border addition is appropriate versus other visual enhancements, nor any prerequisites or exclusions mentioned.

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 states the action ('添加阴影效果') but doesn't disclose behavioral traits like whether this modifies the original image or creates a new one, what the output looks like, performance considerations, or error conditions. For a tool with 7 parameters and no annotations, this leaves significant gaps in understanding how it behaves.

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, efficient phrase ('为图片添加阴影效果') that front-loads the core purpose with zero wasted words. It's appropriately sized for a straightforward image manipulation 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?

Given the tool's moderate complexity (7 parameters, no annotations, but with output schema), the description is minimally adequate. It states what the tool does but lacks context about behavior, alternatives, or usage scenarios. The output schema likely covers return values, but the description doesn't address mutation effects or integration with sibling tools.

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 description adds no parameter information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 7 parameters. The baseline is 3 since the schema does all the work, and the description doesn't compensate with additional context about how parameters interact or typical 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 '为图片添加阴影效果' (Add shadow effect to images) clearly states the verb ('添加阴影效果') and resource ('图片'), making the purpose immediately understandable. It doesn't explicitly differentiate from siblings like 'apply_vignette' or 'create_silhouette' which might also involve shadow-like effects, but the core function is 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 description provides no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (e.g., 'apply_vignette', 'create_silhouette', 'apply_emboss'), there's no indication of when shadow addition is appropriate versus other effects, nor any prerequisites or constraints mentioned.

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. '添加水印' (add watermark) implies a mutation/write operation, but the description doesn't disclose critical behavioral traits: whether the original image is modified in-place or a new file is created, what permissions are needed, potential side effects, or error conditions. For a tool with 7 parameters and no annotations, this is a significant gap in 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 a single, efficient phrase ('为图片添加水印') that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place.

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 moderate complexity (7 parameters, mutation operation) and rich schema (100% coverage) plus output schema existence, the description is minimally adequate but incomplete. It states what the tool does but lacks behavioral context, usage guidelines, and output explanation (though output schema helps). For a mutation tool with no annotations, more disclosure would be beneficial.

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 schema already documents all 7 parameters thoroughly with descriptions, types, defaults, and constraints. The description adds no additional parameter semantics beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 '为图片添加水印' (Add watermark to image) clearly states the verb ('add') and resource ('image'), making the purpose immediately understandable. It distinguishes from siblings like 'add_border' or 'add_shadow' by specifying watermarking. However, it doesn't explicitly mention whether text or image watermarks are supported (though parameters reveal this).

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (e.g., 'add_border', 'apply_blur'), there's no indication of when watermarking is appropriate versus other effects or transformations. No prerequisites, exclusions, or comparison to siblings are mentioned.

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. '调整图片亮度' implies a transformation operation but doesn't disclose whether this modifies the original image or creates a copy, what permissions are needed, performance characteristics, or what the output contains. For a mutation tool with zero annotation coverage, this is insufficient behavioral disclosure.

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 extremely concise - just four characters in Chinese. It's front-loaded with the core purpose and contains zero wasted words. For a simple image adjustment operation, this brevity is appropriate and efficient.

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 that an output schema exists (which presumably describes the return value), the description doesn't need to explain return values. However, for an image transformation tool with no annotations, the description should provide more context about the operation's behavior, side effects, and usage patterns. The current description is minimal but functional given the structured data available.

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 parameters well-documented in the schema. The description adds no parameter information beyond what's already in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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 '调整图片亮度' (adjust image brightness) clearly states the verb (adjust) and resource (image brightness). It's specific about the operation but doesn't differentiate from sibling tools like 'adjust_contrast' or 'adjust_gamma' - all are image adjustment operations. The purpose is clear but lacks sibling distinction.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image adjustment (contrast, gamma, saturation, etc.), there's no indication of when brightness adjustment is appropriate versus other adjustments. No context about prerequisites or exclusions is provided.

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 of behavioral disclosure. It states the action (adjust contrast) but doesn't cover critical aspects like whether this modifies the original image or creates a new one, performance implications, supported image formats, or error handling. For a mutation tool with zero annotation coverage, this leaves significant gaps.

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, efficient phrase ('调整图片对比度') that directly conveys the core function without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 moderate complexity (image processing with two parameters), no annotations, and the presence of an output schema, the description is minimally adequate. It states what the tool does but lacks behavioral context and usage guidance. The output schema likely covers return values, so the description doesn't need to explain those, but it should address mutation behavior and tool selection.

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 description adds no parameter semantics beyond what the input schema provides. Since schema description coverage is 100%, with clear documentation for 'image_source' and 'factor', the baseline score of 3 is appropriate. The description doesn't compensate but doesn't need to given the comprehensive 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?

The description '调整图片对比度' (adjust image contrast) clearly states the verb (adjust) and resource (image contrast), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like adjust_brightness or adjust_saturation, which follow the same pattern, so it lacks sibling distinction.

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 no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where contrast adjustment is preferred over brightness or saturation changes, nor does it reference sibling tools for context. Usage is implied only by the tool name and description.

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 of behavioral disclosure. It mentions the action ('adjust') but doesn't describe what happens to the image (e.g., whether it modifies the original or creates a new image), output format, error conditions, or performance implications. This is inadequate for a mutation tool with zero annotation coverage.

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, efficient phrase ('调整图片伽马值') that directly states the tool's purpose with zero wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 has an output schema (which covers return values), 100% schema description coverage, and no annotations, the description is minimally complete for a simple image adjustment tool. However, it lacks behavioral context (e.g., mutation effects) and usage guidance, leaving gaps that could hinder an agent's understanding.

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 schema fully documents both parameters (image_source and gamma). The description adds no additional parameter information beyond what's in the schema, such as format details or examples. Baseline 3 is appropriate when the schema does all the work.

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 '调整图片伽马值' (adjust image gamma value) clearly states the verb ('adjust') and resource ('image gamma value'), making the purpose immediately understandable. It distinguishes from siblings like adjust_brightness or adjust_contrast by specifying gamma adjustment, though it doesn't explicitly contrast with them in the description text.

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 no guidance on when to use this tool versus alternatives like adjust_brightness or adjust_contrast, nor does it mention prerequisites or typical use cases. It states what the tool does but not when it's appropriate.

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 for behavioral disclosure. While '调整' (adjust) implies mutation, the description doesn't specify whether this modifies the original image or creates a new one, what format the output takes, whether there are performance implications, or any error conditions. For a mutation tool with zero annotation coverage, this is insufficient.

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, efficient phrase in Chinese that directly states the tool's function without any wasted words. It's appropriately sized for a straightforward image adjustment operation.

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 that there's an output schema (though not shown here), the description doesn't need to explain return values. However, for an image mutation tool with no annotations and many similar siblings, the description should provide more context about behavioral characteristics and usage differentiation to be truly 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 100%, with both parameters well-documented in the schema itself (image_source accepts file paths or base64 data, opacity is a 0.0-1.0 number). The description adds no additional parameter information beyond what's already in the schema, so it meets 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 '调整图片不透明度' (adjust image opacity) clearly states the verb ('adjust') and resource ('image opacity'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its many image-processing siblings (like adjust_brightness or adjust_contrast) beyond the specific property being adjusted.

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 no guidance on when to use this tool versus alternatives. With numerous sibling tools for image adjustments (brightness, contrast, saturation, etc.), there's no indication of when opacity adjustment is appropriate versus other visual modifications or how it might interact with them.

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 full burden of behavioral disclosure. While '调整' (adjust) implies a mutation operation, the description doesn't specify whether this modifies the original image or creates a new one, what permissions are needed, or any side effects like performance impacts. It lacks details on output format or error conditions, leaving significant gaps.

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, efficient phrase ('调整图片饱和度') that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple image adjustment tool, making it easy to parse quickly.

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 moderate complexity (image processing with two parameters), the lack of annotations, and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. It states what the tool does but misses behavioral details and usage context, leaving room for improvement without being completely inadequate.

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 description adds no parameter information beyond what's in the input schema, which has 100% coverage with clear descriptions for both parameters (image_source and factor). Since the schema fully documents the parameters, the baseline score of 3 is appropriate—the description doesn't compensate but doesn't need to given the schema's completeness.

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 '调整图片饱和度' (adjust image saturation) clearly states the verb ('adjust') and resource ('image saturation'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'adjust_brightness' or 'adjust_contrast' that perform similar adjustment operations on images, which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image adjustments (e.g., adjust_brightness, adjust_contrast, convert_to_grayscale), there's no indication of appropriate contexts, prerequisites, or exclusions for saturation adjustment specifically.

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 full burden for behavioral disclosure. It states the action ('调整图片锐度') but doesn't describe what the tool actually does: whether it modifies the original image, creates a new image, requires specific image formats, has performance implications, or what the output looks like. For a mutation tool with zero annotation coverage, this is a significant 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, efficient phrase ('调整图片锐度') that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple image processing operation and immediately communicates the core functionality.

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 that there's an output schema (though not shown), the description doesn't need to explain return values. However, for an image manipulation tool with no annotations, the description should provide more context about what the tool actually does to the image and when to use it. The current description is minimal but not completely inadequate given the schema coverage.

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 schema already fully documents both parameters. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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 '调整图片锐度' (Adjust image sharpness) clearly states the verb (adjust) and resource (image sharpness). It distinguishes from siblings like 'adjust_brightness' or 'apply_sharpen' by focusing specifically on sharpness adjustment rather than brightness or a fixed sharpening operation. However, it doesn't explicitly differentiate from 'apply_sharpen' which might be a similar operation.

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 no guidance on when to use this tool versus alternatives like 'apply_sharpen' or other adjustment tools. There's no mention of use cases, prerequisites, or comparisons with sibling tools. The agent must infer usage from the tool name alone.

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 of behavioral disclosure. '应用模糊滤镜' implies a transformation operation but doesn't disclose whether it modifies the original image, creates a new file, what formats are supported, performance characteristics, or error conditions. For a mutation tool with zero annotation coverage, this is insufficient.

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 extremely concise at just three characters in Chinese ('应用模糊滤镜'), which translates efficiently to 'apply blur filter'. It's front-loaded with the core action and contains zero wasted words.

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 that there's an output schema (which handles return values) and 100% schema description coverage, the description is minimally adequate. However, for an image processing tool that likely creates modified images, more context about the transformation behavior would be helpful despite the structured data coverage.

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 parameters well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 '应用模糊滤镜' (apply blur filter) clearly states the verb ('apply') and resource ('blur filter'), making the purpose immediately understandable. It distinguishes from siblings like 'apply_gaussian_blur' by being a general blur operation, though it doesn't explicitly differentiate from that specific sibling.

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 on when to use this tool versus alternatives like 'apply_gaussian_blur' or other image processing tools. The description only states what it does, not when it's appropriate or what distinguishes it from similar operations.

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 full burden of behavioral disclosure. It only states the action ('apply contour filter') without any details on effects (e.g., visual outcome, whether it modifies the image in place or creates a new version), performance implications, error conditions, or output format. This is inadequate for a tool that performs image processing with potential 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.

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

The description is a single phrase ('应用轮廓滤镜'), which is very concise and front-loaded with the core action. There is no wasted verbiage, making it efficient in terms of length. However, it may be overly terse given the lack of additional context needed for clarity and usage.

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 complexity of image processing and the lack of annotations, the description is incomplete. It doesn't explain what a 'contour filter' does, how it differs from similar tools, or what the output entails. While an output schema exists (which might define the return structure), the description fails to provide sufficient context for an agent to understand the tool's behavior and appropriate use cases, especially among many siblings.

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 input schema has 100% description coverage, with the single parameter 'image_source' well-documented as accepting file paths or base64-encoded image data. The description adds no additional meaning beyond this, as it doesn't elaborate on parameter usage or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter semantics effectively.

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 '应用轮廓滤镜' (apply contour filter) states a clear verb+resource combination, indicating it applies a contour filter to an image. However, it doesn't distinguish this tool from its many sibling image processing tools (like apply_edge_enhance, apply_find_edges, etc.), leaving the specific nature of 'contour' ambiguous compared to similar edge-related filters.

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 no guidance on when to use this tool versus alternatives. With numerous sibling tools for image effects (e.g., apply_edge_enhance, apply_emboss, apply_find_edges), there is no indication of what makes 'contour' unique or when it's preferred over other edge or filter operations, nor any mention of prerequisites or context for its application.

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 full burden of behavioral disclosure. It states the action ('apply edge enhance filter') but doesn't describe what the tool does beyond that—such as whether it modifies the image in place, returns a new image, has side effects, or requires specific permissions. For a mutation tool with zero annotation coverage, this is a significant 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, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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 that there's an output schema (which likely describes the result), the description doesn't need to explain return values. However, as a mutation tool with no annotations and incomplete behavioral transparency, the description should do more to clarify effects and usage. It's minimally adequate but leaves gaps in understanding the tool's behavior.

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 description coverage is 100%, with the parameter 'image_source' fully documented in the schema as accepting file paths or base64-encoded image data. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline of 3 without compensating for any gaps.

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 verb 'apply' and the resource 'edge enhance filter', making the purpose understandable. It distinguishes this tool from siblings like 'apply_blur' or 'apply_sharpen' by specifying the particular filter type. However, it doesn't explicitly state what edge enhancement does (e.g., accentuates edges in an image), which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when edge enhancement is appropriate (e.g., for emphasizing outlines) or when other tools like 'apply_sharpen' or 'apply_contour' might be better. There's no context about prerequisites or exclusions.

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 of behavioral disclosure. It states the action ('apply') but doesn't describe what the tool actually does behaviorally: whether it modifies the image in-place, returns a new image, requires specific image formats, has performance implications, or what the output looks like. For a tool with no annotations, this is a significant gap in 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 extremely concise—just four characters in Chinese ('应用浮雕滤镜'). It's front-loaded with the core action and resource, with zero wasted words. This is appropriately sized for a simple, single-purpose 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?

Given the tool's moderate complexity (image processing filter), no annotations, and the presence of an output schema (which likely describes the result), the description is minimally adequate. It states what the tool does but lacks context about behavior, usage, or effects. The output schema may cover return values, but the description doesn't provide enough standalone guidance for effective use.

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 description adds no parameter-specific information beyond what's in the schema. However, with 100% schema description coverage (the single parameter 'image_source' is well-documented in the schema), the baseline score is 3. The description doesn't compensate but doesn't need to since the schema fully covers the parameter semantics.

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 '应用浮雕滤镜' (apply emboss filter) clearly states the verb ('apply') and resource ('emboss filter'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'apply_blur' or 'apply_sepia' by specifying the exact filter type. However, it doesn't explicitly mention the target resource (image), which is implied but could be more explicit.

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 no guidance on when to use this tool versus alternatives. It doesn't explain what an emboss filter does, when it's appropriate (e.g., for artistic effects, texture enhancement), or how it differs from similar tools like 'apply_contour' or 'apply_edge_enhance' in the sibling list. There's no mention of prerequisites or context for usage.

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 full burden. It mentions applying a filter but doesn't disclose behavioral traits such as whether it modifies the original image, requires specific image formats, has performance implications, or what the output looks like. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 has an output schema (which handles return values), no annotations, and high schema coverage for its single parameter, the description is minimally adequate. However, as a filter application tool with no behavioral disclosure, it lacks completeness in explaining effects or usage context, scoring at the minimum viable level.

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 the parameter 'image_source' well-documented in the schema as accepting file paths or base64-encoded image data. The description adds no additional meaning beyond what the schema provides, so it meets the baseline of 3 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 '应用边缘检测滤镜' (apply edge detection filter) states a clear verb ('apply') and resource ('edge detection filter'), but it's somewhat vague about what 'edge detection' entails and doesn't distinguish from sibling tools like 'apply_contour' or 'apply_edge_enhance'. It's functional but lacks specificity about the algorithmic approach or visual effect.

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 no guidance on when to use this tool versus alternatives like 'apply_contour' or 'apply_edge_enhance', nor does it mention prerequisites or exclusions. It's a standalone statement with no contextual usage information.

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 full burden but lacks behavioral details. It doesn't disclose whether this is a read-only or destructive operation, how it handles errors (e.g., invalid image sources), performance implications, or output characteristics beyond what the output schema might cover.

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, efficient sentence in Chinese ('应用高斯模糊滤镜') that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with zero waste.

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 moderate complexity (image processing with two parameters), no annotations, and the presence of an output schema, the description is minimally adequate. It states what the tool does but lacks context on usage, behavioral traits, or integration with siblings, leaving gaps that could hinder agent selection.

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 parameters are fully documented in the schema. The description adds no additional meaning beyond the schema's details for 'image_source' and 'radius', meeting the baseline for high coverage without extra value.

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 '应用高斯模糊滤镜' (Apply Gaussian blur filter) clearly states the action (apply) and the specific effect (Gaussian blur filter). It distinguishes from siblings like 'apply_blur' (generic blur) by specifying the Gaussian type, though it doesn't explicitly contrast with all similar tools like 'apply_smooth' or 'apply_sharpen'.

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 on when to use this tool versus alternatives. It doesn't mention scenarios where Gaussian blur is preferred over other blur types (e.g., 'apply_blur') or other image effects, nor does it specify prerequisites or exclusions for usage.

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 full burden but offers minimal behavioral information. It states the action ('apply invert filter') but doesn't disclose whether this is a destructive operation, what permissions are needed, rate limits, or output format. The agent must infer behavior from context, leaving significant gaps.

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, efficient phrase ('应用反色滤镜') that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, earning full marks for conciseness.

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 low complexity (one parameter, simple operation), the presence of an output schema (which handles return values), and 100% schema coverage, the description is minimally adequate. However, it lacks context on behavioral aspects like side effects or error conditions, making it incomplete for safe use without annotations.

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 the parameter 'image_source' well-documented as accepting file paths or base64 data. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3. No additional semantics are offered.

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 '应用反色滤镜' (apply invert filter) clearly states the verb 'apply' and the resource 'invert filter', making the purpose immediately understandable. It distinguishes from siblings like 'apply_blur' or 'apply_sepia' by specifying the specific filter type. However, it doesn't explicitly mention it operates on images, though this is implied by context.

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 no guidance on when to use this tool versus alternatives. It doesn't mention what 'invert' does (negating colors), when it's appropriate (e.g., for artistic effects or enhancing contrast), or suggest sibling tools like 'convert_to_grayscale' for different transformations. Usage is implied only by the tool name in context.

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 full burden of behavioral disclosure. While 'apply' implies a transformation operation, the description doesn't reveal whether this is destructive to the original image, what permissions are needed, whether it has rate limits, or what the output format will be. For a mutation tool with zero annotation coverage, this is a significant 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, efficient phrase that directly states the tool's function without any wasted words. It's appropriately sized for a straightforward image filter operation and is front-loaded with the essential information.

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 moderate complexity (image transformation), the presence of an output schema (which handles return values), and 100% schema coverage for the single parameter, the description is minimally adequate. However, the lack of annotations and behavioral context means it doesn't fully prepare the agent for safe and effective use.

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 the single parameter 'image_source' well-documented in the schema as accepting file paths or base64 data. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline score of 3 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 '应用复古棕褐色滤镜' (Apply retro sepia filter) clearly states the verb 'apply' and the resource 'sepia filter', making the purpose immediately understandable. It distinguishes from siblings like 'apply_invert' or 'apply_blur' by specifying the particular filter type. However, it doesn't explicitly mention this operates on images, though that's implied by context.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when sepia filtering is appropriate compared to other color adjustments like 'adjust_brightness' or 'convert_to_grayscale', nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name alone.

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 of behavioral disclosure but only states the action without explaining effects (e.g., how much sharpening is applied, whether it's reversible, or if it modifies the original image). It doesn't mention performance implications, output format, or error handling, leaving significant gaps for a mutation tool.

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 extremely concise with a single phrase ('应用锐化滤镜'), front-loading the core action without any wasted words. It efficiently communicates the tool's purpose in minimal space.

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 has an output schema (which handles return values) and high schema coverage, the description's minimalism is partially excused. However, as a mutation tool with no annotations, it should provide more behavioral context (e.g., effects on the image, typical use cases) to be fully complete for safe agent use.

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 description coverage is 100%, with the single parameter 'image_source' well-documented in the schema as accepting file paths or base64 data. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline for adequate coverage without adding value.

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 '应用锐化滤镜' (apply sharpen filter) clearly states the verb (apply) and resource (sharpen filter), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'adjust_sharpness' or 'apply_edge_enhance', which might have overlapping functionality in image processing contexts.

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 no guidance on when to use this tool versus alternatives like 'adjust_sharpness' or other image enhancement tools in the sibling list. It lacks any context about specific scenarios, prerequisites, or comparisons with similar tools.

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 full burden of behavioral disclosure. It only states the action ('apply smooth filter') without detailing effects (e.g., how much smoothing, whether it's reversible, performance implications, or output format). For an image processing tool with no annotation coverage, this is a significant gap in transparency about its behavior and constraints.

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 phrase '应用平滑滤镜', which is extremely concise and front-loaded with the core action. There is no wasted text, and it directly communicates the tool's function without unnecessary elaboration, making it efficient for quick understanding.

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 complexity (simple image filter), 100% schema coverage, and the presence of an output schema (implied by 'Has output schema: true'), the description is minimally adequate. It states what the tool does but lacks details on effects, usage context, or behavioral traits. With output schema handling return values, the description meets a basic threshold but leaves gaps in guidance and transparency.

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 description adds no parameter information beyond what the schema provides. The input schema has 100% description coverage, clearly documenting the 'image_source' parameter as accepting file paths or base64 data. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding but doesn't detract either.

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 '应用平滑滤镜' (apply smooth filter) clearly states the verb 'apply' and the resource 'smooth filter', making the purpose understandable. It distinguishes from siblings like apply_blur or apply_sharpen by specifying 'smooth' as the filter type. However, it doesn't explicitly differentiate from similar tools like apply_gaussian_blur, which might also smooth images, leaving some ambiguity.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when smooth filtering is appropriate (e.g., for noise reduction or softening edges) or when not to use it (e.g., for sharp details). With many sibling tools like apply_blur and apply_sharpen, this lack of context makes it hard for an agent to choose correctly without additional information.

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 for behavioral disclosure. It only states the action ('apply vignette effect') without mentioning side effects, permissions, rate limits, or output behavior. For a mutation tool (applies visual effect to an image), this lack of transparency about what changes occur and any constraints is a significant 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, efficient phrase in Chinese that directly states the tool's function. It's appropriately sized and front-loaded with zero wasted words, making it easy to parse quickly.

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 has an output schema (implied by 'Has output schema: true'), the description doesn't need to explain return values. However, as a mutation tool with no annotations and many sibling alternatives, the description is minimally adequate but lacks context about behavioral traits and usage differentiation, leaving clear gaps for the agent.

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 parameters are fully documented in the schema. The description adds no additional meaning about parameters beyond what's in the schema (e.g., no examples, no clarification of 'image_source' formats beyond file path/base64). Baseline 3 is appropriate when the schema does the heavy lifting.

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 '应用晕影效果' (apply vignette effect) states the verb and resource clearly but is vague about scope and differentiation. It doesn't specify whether this applies to a single image or batch, nor how it differs from similar sibling tools like 'apply_blur' or 'apply_edge_enhance'. The purpose is understandable but lacks specificity for sibling differentiation.

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 on when to use this tool versus alternatives. With many sibling image processing tools (e.g., apply_blur, adjust_brightness), the description offers no context about appropriate use cases, prerequisites, or exclusions. The agent must infer usage from the tool name alone.

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 full burden but only states what the tool does ('batch resize images') without disclosing behavioral traits. It doesn't mention whether this is a read-only or destructive operation, what permissions might be needed, how errors are handled, whether it modifies originals or creates copies, or any performance/rate limit considerations for batch processing.

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, efficient phrase ('批量调整图片大小') that directly states the tool's purpose with zero waste. It's appropriately sized and front-loaded, though extremely brief.

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 has 5 parameters, no annotations, but has an output schema and 100% schema coverage, the description is minimally adequate. However, for a batch processing tool that likely has performance implications and error handling considerations, the description should provide more context about what 'batch' means operationally and how it differs from single-image alternatives.

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 schema already documents all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description doesn't compensate with any extra context about parameter interactions or usage patterns.

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 '批量调整图片大小' (Batch resize images) clearly states the verb (resize) and resource (images) with the batch operation scope. It distinguishes from the sibling 'resize_image' which appears to be a single-image version, though this distinction isn't explicitly stated in the description itself.

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 no guidance on when to use this tool versus alternatives like 'resize_image' (for single images) or other image manipulation siblings. There's no mention of prerequisites, performance considerations, or when batch processing is preferred over individual operations.

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 full burden of behavioral disclosure. '混合两张图片' implies a mutation operation that creates a new blended image, but it doesn't describe what happens to the input images (preserved or modified), whether there are size/format constraints, performance implications, or error conditions. For a tool with 5 parameters and no annotations, this is a significant gap in 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 extremely concise—just four characters in Chinese—and front-loaded with the core action. There's zero wasted verbiage, making it efficient for quick understanding. Every character earns its place by directly stating the tool's purpose.

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 (5 parameters, mutation operation) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and many sibling tools, it lacks context about behavioral traits, usage scenarios, and differentiation. The output schema mitigates some completeness issues, but the description alone is insufficient for full understanding.

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 description coverage is 100%, with each parameter well-documented in the input schema (e.g., blend_mode options, opacity range, output formats). The description adds no additional parameter semantics beyond what's already in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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 '混合两张图片' (blend two images) clearly states the verb (blend) and resource (images), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from similar sibling tools like 'create_collage' or 'apply_overlay' (if such existed), which might also involve combining images. The description is specific about the action but lacks sibling differentiation.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (e.g., 'add_watermark', 'create_collage', 'apply_overlay'), there's no indication of when blending is preferred over other methods for combining images. It lacks any context about use cases, prerequisites, or exclusions.

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. '转换图片格式' implies a mutation operation that changes image format, but it doesn't disclose behavioral traits like whether it preserves metadata, handles errors, or has performance implications. This is a significant gap for a tool with no annotation coverage.

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 phrase '转换图片格式' (convert image format), which is extremely concise and front-loaded with the core purpose. There's zero wasted text, making it efficient for quick understanding.

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 that an output schema exists, the description doesn't need to explain return values. However, with no annotations and a mutation tool (format conversion), the description is too minimal—it doesn't cover behavioral aspects like side effects or error handling, leaving gaps despite the good schema coverage.

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 schema already documents all parameters (image_source, target_format, quality). The description adds no additional meaning beyond what's in the schema, such as explaining parameter interactions or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 '转换图片格式' (convert image format) clearly states the verb (convert) and resource (image format), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'convert_to_grayscale' or 'create_gif' which also involve format transformations, so it lacks sibling differentiation.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'convert_to_grayscale', 'create_gif'), there's no indication of when format conversion is appropriate versus other transformations or creations.

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 for behavioral disclosure. It states the transformation outcome (grayscale) but doesn't mention whether this is a destructive operation, what permissions are needed, how large images are handled, or what the output looks like. For an image processing tool with zero annotation coverage, this leaves significant behavioral gaps.

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 extremely concise with just one sentence in Chinese: '将图片转换为灰度图' (convert image to grayscale). It's front-loaded with the core purpose and contains zero wasted words. This is an excellent example of minimal but complete statement of function.

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 has an output schema (which handles return values), 100% parameter schema coverage, and relatively simple functionality, the description is minimally complete. However, with no annotations and many sibling tools, it should ideally provide more context about when to choose grayscale versus other color transformations. The description covers the basic purpose but lacks richer contextual guidance.

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 the single parameter 'image_source' well-documented in the schema as accepting file paths or base64 data. The description doesn't add any parameter semantics beyond what the schema already provides, so it meets the baseline of 3 for high schema coverage without adding extra value.

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's purpose: '将图片转换为灰度图' (convert image to grayscale). It specifies the action (convert) and the resource (image), but doesn't explicitly differentiate from sibling tools like 'apply_sepia' or 'apply_invert' which also modify image colors. The purpose is clear but lacks sibling distinction.

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 no guidance on when to use this tool versus alternatives. With many sibling tools that modify image appearance (e.g., apply_sepia, adjust_saturation, apply_invert), there's no indication of when grayscale conversion is appropriate versus other color transformations. No context or exclusions are mentioned.

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 for behavioral disclosure. The description reveals nothing about what the tool actually does behaviorally - no information about permissions needed, whether it modifies source images, rate limits, output characteristics beyond format, error conditions, or performance implications. It's a minimal phrase that fails to describe the tool's operational 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?

The description is extremely concise - a single Chinese phrase that directly states the tool's function. While it's under-specified for completeness, as pure conciseness it's maximally efficient with zero wasted words or redundant information.

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 has 5 parameters, no annotations, and operates in a domain with many similar sibling tools, the description is severely incomplete. While an output schema exists (which helps with return values), the description fails to provide necessary context about when to use this tool, what it actually produces, or how it differs from alternatives. For a creative/mutation tool with multiple parameters, this minimal description is inadequate.

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?

With 100% schema description coverage, all parameters are well-documented in the input schema itself. The description adds no parameter information beyond what's already in the schema - it doesn't explain relationships between parameters, provide usage examples, or clarify edge cases. The baseline of 3 is appropriate since the schema does the heavy lifting, but the description contributes nothing additional.

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 '创建图片拼贴' (Create image collage) is a tautology that essentially restates the tool name 'create_collage' in Chinese. It provides no additional specificity about what kind of collage, what resources it uses, or how it differs from sibling tools like 'create_thumbnail_grid' or 'blend_images'. The purpose is stated but lacks distinguishing details.

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 absolutely no guidance on when to use this tool versus alternatives. With multiple sibling tools for image manipulation (e.g., 'create_thumbnail_grid', 'blend_images', 'create_gif'), there is no indication of when this specific collage creation tool is appropriate versus other composition or formatting tools.

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 states the tool creates GIFs but doesn't disclose behavioral traits like whether it overwrites existing files, requires specific permissions, has rate limits, or what the output format/quality is. The description is minimal and lacks operational context needed for safe invocation.

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 extremely concise ('创建GIF动画') with no wasted words, but it's arguably under-specified rather than optimally concise. It's front-loaded with the core purpose, though more detail would improve utility without sacrificing brevity.

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 has an output schema (which handles return values) and full schema coverage for parameters, the description's minimalism is partially mitigated. However, for a mutation tool (creating GIFs) with no annotations, the description should provide more context about behavior, side effects, and usage scenarios to be 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 100%, so the schema fully documents all three parameters (image_sources, duration, loop). The description adds no parameter semantics beyond what's in the schema, maintaining the baseline score of 3 since the schema does the heavy lifting.

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 '创建GIF动画' (Create GIF animation) states the basic purpose (verb+resource) but is vague about scope and mechanism. It doesn't specify whether this creates GIFs from image sequences, video, or other sources, nor does it distinguish this tool from sibling tools like 'create_collage' or 'create_thumbnail_grid' which also create composite outputs from 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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing image files first), use cases (e.g., for animations vs static images), or comparisons to sibling tools like 'create_collage' for different output types.

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 full burden of behavioral disclosure. The description only states what the tool creates ('宝丽来风格效果') but doesn't disclose any behavioral traits: whether this modifies the original image, creates a new file, what permissions are needed, performance characteristics, or error conditions. For a tool with 4 parameters and no annotation coverage, this is a significant 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 extremely concise - a single phrase '创建宝丽来风格效果' that states the core purpose. There's zero wasted language. However, it's arguably too concise given the tool's complexity and lack of other documentation, bordering on under-specification rather than optimal conciseness.

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 that there's an output schema (though not provided here), the description doesn't need to explain return values. However, for an image processing tool with 4 parameters, no annotations, and many sibling alternatives, the description is minimally adequate but has clear gaps in usage guidance and behavioral context. It provides the basic purpose but lacks the completeness needed for optimal agent understanding.

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 schema already documents all 4 parameters with good descriptions. The tool description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description doesn't compensate with any parameter context.

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 '创建宝丽来风格效果' (Create Polaroid-style effect) states what the tool does with a specific verb ('创建' - create) and resource ('宝丽来风格效果' - Polaroid-style effect). However, it doesn't distinguish this tool from its many sibling image processing tools, particularly those like 'add_border' or 'add_shadow' that might implement similar visual effects. The purpose is clear but lacks sibling differentiation.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image manipulation (add_border, add_shadow, apply_vignette, etc.), there's no indication of when this specific Polaroid effect is appropriate versus combining other tools. No context, prerequisites, or exclusions are mentioned.

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 states what the tool does ('create silhouette effect') but doesn't disclose behavioral traits such as whether it modifies the original image, requires specific file permissions, has performance implications, or what the output contains (though output schema exists). For a mutation tool with zero annotation coverage, this is inadequate.

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, efficient sentence in Chinese that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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 (image processing with 5 parameters), the description is minimal but complete enough in context. The schema covers parameters fully, and an output schema exists, so the description doesn't need to explain return values. However, for a tool with no annotations, it could benefit from more behavioral context to reach a perfect score.

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 schema already documents all parameters thoroughly. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or providing usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 '创建图片的剪影效果' (Create silhouette effect for images) clearly states the verb ('create') and resource ('silhouette effect for images'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'apply_contour' or 'apply_edge_enhance' which might also produce edge-based effects, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for image processing (e.g., 'apply_contour', 'convert_to_grayscale'), there's no indication of when a silhouette effect is preferred over other transformations or what specific use cases it serves.

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 full burden for behavioral disclosure but provides none. It doesn't indicate whether this is a read-only or destructive operation, what permissions might be needed, what happens with invalid inputs, or any rate limits. The description doesn't even mention that this tool outputs an image file, though the output schema would reveal this.

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?

While technically concise with just three Chinese characters, this is under-specification rather than effective conciseness. The single phrase doesn't provide enough information to be helpful. Every sentence should earn its place, but here the single phrase doesn't earn its place by providing meaningful guidance.

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 (6 parameters, image processing operation) and lack of annotations, the description is inadequate. While an output schema exists (which would reveal the return type), the description doesn't provide the minimal context needed for an agent to understand when and why to use this tool versus alternatives. For a tool with multiple parameters and sibling alternatives, this description is incomplete.

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 schema already fully documents all 6 parameters. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 '创建缩略图网格' (Create thumbnail grid) is a tautology that restates the tool name in Chinese without adding specificity. It doesn't distinguish this tool from sibling tools like 'create_collage' or 'create_gif' that also create composite images. While the verb 'create' and resource 'thumbnail grid' are present, the description lacks details about what makes this tool unique.

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 no guidance on when to use this tool versus alternatives. With multiple sibling tools for image composition (create_collage, create_gif, create_polaroid), there's no indication of when a thumbnail grid is appropriate versus other formats. No prerequisites, exclusions, or comparison to other tools are mentioned.

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 full burden of behavioral disclosure. '裁剪图片' only states the action without any information about side effects (e.g., whether it modifies the original image, creates a new file, requires specific permissions, or has rate limits). For a mutation tool with zero annotation coverage, this is a critical gap that leaves the agent guessing about important behavioral traits.

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?

While concise with just two characters, this is under-specification rather than effective brevity. The description fails to convey necessary information and doesn't use its limited space efficiently—it could include key details without becoming verbose. It's too terse to be helpful.

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 (image manipulation with 5 required parameters), lack of annotations, and presence of an output schema, the description is incomplete. It doesn't address behavioral aspects, usage context, or output expectations. The output schema might cover return values, but the description should still provide operational context, which it fails to do.

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 all parameters clearly documented in the input schema (image_source, left, top, right, bottom). The description adds no additional meaning beyond what the schema provides—it doesn't explain coordinate systems, units, or relationships between parameters. Given the high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 '裁剪图片' (crop image) is a tautology that merely restates the tool name in Chinese without adding any meaningful clarification. It doesn't specify what resource is being cropped (images), what the output is, or how it differs from sibling tools like 'resize_image' or 'create_thumbnail_grid'. The purpose is implied but not explicitly stated beyond the name.

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 no guidance on when to use this tool versus alternatives. There are many sibling tools for image manipulation (e.g., resize_image, rotate_image, apply_crop might be implied but not listed), but the description offers no context, prerequisites, or comparisons. It's completely silent on usage scenarios.

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 full burden of behavioral disclosure. It states what the tool does ('extract main colors') but doesn't describe how it behaves: e.g., whether it returns color codes (like RGB/HEX), how colors are determined (e.g., clustering algorithms), if it modifies the image, or any performance considerations. This leaves gaps for an AI agent to understand the tool's operation.

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, efficient phrase ('提取图片主要颜色') that directly conveys the core function without unnecessary words. It's front-loaded and wastes no space, making it easy for an AI agent to parse quickly.

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 moderate complexity (extracting colors from images), no annotations, and an output schema (which handles return values), the description is minimally adequate. It states the purpose but lacks behavioral details and usage context. With output schema covering returns, the description doesn't need to explain outputs, but it should provide more operational guidance to be fully 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 description adds no parameter-specific information beyond what's in the input schema, which has 100% coverage with clear descriptions for all three parameters (image_source, num_colors, output_format). Since schema coverage is high, the baseline is 3, as the schema adequately documents parameters without needing extra details in the description.

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 '提取图片主要颜色' (Extract main colors from an image) clearly states the verb ('extract') and resource ('main colors from an image'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_image_info' or 'convert_to_grayscale', which might also involve color analysis, though those have different primary functions.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an image source), exclusions, or compare it to siblings like 'get_image_info' that might provide color-related data. Usage is implied by the purpose 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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure but fails to add any context. It doesn't describe what the tool does beyond the basic action (e.g., whether it modifies the original image, creates a new file, handles errors, or has performance implications). For a mutation tool with zero annotation coverage, this is a significant 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 overly concise to the point of under-specification—it's a single phrase with no structure or elaboration. While brief, it fails to convey essential information, making it inefficient rather than appropriately concise. Every sentence should earn its place, but here the lack of content is a drawback.

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 (image transformation with two parameters) and the presence of an output schema (which reduces the need to explain return values), the description is incomplete. It lacks behavioral context, usage guidelines, and differentiation from siblings, making it inadequate for effective tool selection and invocation despite the structured data.

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 description coverage is 100%, with clear documentation for both parameters ('image_source' and 'direction'), including allowed values for direction. The description adds no parameter semantics beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting.

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 '翻转图片' (flip image) is a tautology that merely restates the tool name in Chinese without adding any meaningful clarification. While it indicates the tool performs image flipping, it doesn't specify what resource it operates on (e.g., an image file or data) or differentiate it from similar sibling tools like 'rotate_image' or other transformation tools.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'rotate_image' for different transformations or clarify specific scenarios for flipping (e.g., mirroring effects). There's no indication of prerequisites, exclusions, or contextual usage.

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 full burden of behavioral disclosure. It doesn't describe what 'basic information' entails (e.g., dimensions, format, metadata), whether it's a read-only operation, or any performance implications. For a tool with no annotations, this is a significant gap in 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 a single, efficient phrase in Chinese ('获取图片基本信息'), which is appropriately concise and front-loaded. It wastes no words, though it could benefit from more detail to improve clarity. The structure is minimal but effective for its brevity.

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 low complexity (one parameter) and the presence of an output schema, the description is somewhat complete but lacks depth. It doesn't explain what 'basic information' includes, which is crucial since no annotations are provided. The output schema likely covers return values, but the description should still clarify the tool's scope to aid agent selection.

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 input schema has 100% description coverage, clearly documenting the 'image_source' parameter as accepting file paths or base64 data. The description adds no additional parameter semantics beyond what the schema provides. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.

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 '获取图片基本信息' (Get basic image information) states a clear purpose with a verb and resource, but it's vague about what 'basic information' includes. It doesn't distinguish from sibling tools like 'extract_colors' or 'get_performance_stats', which might also provide image-related information. The purpose is understandable but lacks specificity.

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 on when to use this tool versus alternatives. The description doesn't mention context, prerequisites, or exclusions. Given sibling tools like 'extract_colors' or 'get_performance_stats', there's no indication of how this tool differs in usage, leaving the agent to guess based on the name alone.

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 for behavioral disclosure. The description only states what the tool does ('get performance statistics') without revealing any behavioral traits: it doesn't specify whether this is a read-only operation, what permissions might be needed, whether it's resource-intensive, what format the statistics are returned in, or if there are any side effects. For a tool with no annotation coverage, this is a significant gap in 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 extremely concise - a single phrase in Chinese. For a zero-parameter tool that presumably returns performance data, this brevity is appropriate. There's no wasted language or unnecessary elaboration, and the meaning is immediately clear (though incomplete as noted in other dimensions).

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 that the tool has 0 parameters (simple input), no annotations, but has an output schema (which should document return values), the description is minimally adequate but has clear gaps. The output schema will handle return value documentation, so the description doesn't need to explain that. However, for a tool in a context with many image processing siblings, it should better distinguish itself and provide more behavioral context given the lack of annotations.

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 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). With no parameters, the description doesn't need to compensate for schema gaps. The baseline for zero parameters is 4, as there's no parameter semantics to explain beyond what the empty schema already provides.

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 '获取性能统计信息' (Get performance statistics) is a tautology that essentially restates the tool name 'get_performance_stats' in Chinese. It doesn't specify what kind of performance statistics (image processing? system? application?), what resource they apply to, or how this differs from sibling tools like 'get_image_info' or 'reset_performance_stats'. The purpose is vague and lacks differentiation.

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 no guidance on when to use this tool versus alternatives. There's no mention of context, prerequisites, or comparisons to sibling tools like 'get_image_info' (which might provide different metadata) or 'reset_performance_stats' (which appears to be a related mutation tool). Without any usage instructions, the agent has no basis for selecting this tool appropriately.

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 of behavioral disclosure. It states what the tool does (loads images) but doesn't describe behavioral traits such as error handling (e.g., invalid paths/formats), performance implications, memory usage, or output format. For a tool that likely serves as an entry point for image processing, this is a significant 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, efficient sentence in Chinese that directly states the tool's function without any fluff or redundant information. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 low complexity (one parameter) and the presence of an output schema (which likely describes the loaded image data), the description is minimally adequate. However, it lacks context on integration with sibling tools (e.g., that this loads images for subsequent processing) and behavioral details, leaving gaps in completeness for a foundational tool in this server.

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 description coverage is 100%, with the parameter 'source' well-documented in the schema itself (describing it as a file path or base64 string). The description adds no additional semantic meaning beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

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 action ('加载' meaning 'load') and the resource ('图片文件或base64编码的图片' meaning 'image file or base64-encoded image'), making the purpose immediately understandable. It distinguishes from siblings by focusing on loading/input rather than processing or output operations, though it doesn't explicitly name 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 description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that this should be used before applying other image processing tools), exclusions, or contextual cues for selection among siblings like 'get_image_info' or 'save_image'.

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. 'Reset' implies a mutation that clears or restores data, but it doesn't disclose behavioral traits such as whether this requires specific permissions, what exactly gets reset (e.g., counters, logs), if it's destructive or reversible, or any rate limits. The description is minimal and lacks critical operational details.

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 phrase '重置性能统计信息', which is very concise and front-loaded with the core action. There's no wasted text, but it might be overly terse given the lack of additional context. It earns its place by stating the purpose directly.

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 has no parameters, an output schema exists, and no annotations are provided, the description is incomplete. It doesn't explain what 'performance statistics' entail, what resetting does (e.g., sets to zero, clears history), or any side effects. The output schema might cover return values, but the description fails to provide enough context for safe and effective use.

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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, but it also doesn't compensate for any gaps since none exist. A baseline of 4 is appropriate as the description doesn't introduce confusion about parameters.

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 '重置性能统计信息' (Reset performance statistics) states a clear verb ('reset') and resource ('performance statistics'), but it's vague about what exactly gets reset. It doesn't distinguish from sibling tools like 'get_performance_stats', which might retrieve the same statistics. The purpose is understandable but lacks specificity about scope or impact.

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 on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing performance stats to exist), exclusions, or related tools like 'get_performance_stats'. The description alone offers no context for usage decisions.

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 full burden for behavioral disclosure. '调整图片大小' only states the basic function without mentioning whether this modifies the original image, creates a new file, what formats are supported, performance characteristics, or error conditions. It lacks crucial context about what actually happens during the resize operation.

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 maximally concise with just three Chinese characters that directly convey the core function. There's zero wasted text, and it's perfectly front-loaded with the essential information.

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 has an output schema and 100% schema description coverage, the description doesn't need to explain return values or parameters. However, as a mutation tool with no annotations, it should provide more behavioral context about what the operation actually does (creates new file, modifies in-place, supported formats, etc.). The presence of output schema helps but doesn't fully compensate for the lack of operational transparency.

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 schema already documents all 5 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation through the schema alone.

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 '调整图片大小' (resize image) clearly states the verb and resource. It distinguishes from siblings like 'crop_image', 'rotate_image', or 'batch_resize' by focusing specifically on resizing. However, it doesn't explicitly differentiate from 'create_thumbnail_grid' which might involve resizing as part of its operation.

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 no guidance on when to use this tool versus alternatives. It doesn't mention when resizing is appropriate compared to cropping, rotating, or using batch operations. There's no context about prerequisites, limitations, or typical use cases.

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 full burden. It states the action 'rotate' but doesn't disclose behavioral traits like whether it modifies the original image, requires specific permissions, handles errors, or has performance implications. This is a significant gap for a mutation tool with zero annotation coverage.

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 phrase '旋转图片', which is concise and front-loaded. However, it's under-specified rather than efficiently informative, as it lacks necessary context for a tool with multiple parameters and sibling alternatives.

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 has an output schema (not provided in details but indicated as true), the description needn't explain return values. However, with 4 parameters, no annotations, and many sibling tools, the description is incomplete—it doesn't cover usage context or behavioral aspects, making it minimally adequate but with clear gaps.

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 schema already documents all parameters (image_source, angle, expand, fill_color) with details like formats and defaults. The description adds no meaning beyond what the schema provides, but the baseline is 3 when schema coverage is high.

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 '旋转图片' (rotate image) states the basic action but is vague. It specifies the verb 'rotate' and resource 'image' but lacks details about what gets rotated (e.g., by angle) or how it distinguishes from siblings like 'flip_image'. It's not tautological but minimal.

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 on when to use this tool versus alternatives is provided. The description doesn't mention context, prerequisites, or exclusions, such as when to choose 'rotate_image' over 'flip_image' or other image manipulation tools in the sibling list.

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 states the action ('save') but lacks critical behavioral details: it doesn't specify if the tool overwrites existing files, requires write permissions, handles errors (e.g., invalid paths), or has performance implications. For a write operation with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence in Chinese ('保存图片到指定路径') that directly states the tool's purpose with zero waste. It's front-loaded and appropriately sized for a straightforward save operation, earning full marks for conciseness.

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 moderate complexity (4 parameters, write operation) and rich schema (100% coverage, output schema exists), the description is minimally adequate. It states the core purpose but lacks behavioral context (e.g., file overwriting, error handling) and usage guidelines. The output schema reduces the need to explain return values, but for a mutation tool with no annotations, more completeness is warranted.

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 schema fully documents all parameters (image_source, output_path, format, quality). The description adds no additional parameter semantics beyond what's in the schema. Baseline 3 is appropriate when the schema does all the heavy lifting, but the description doesn't compensate or enhance understanding.

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 '保存图片到指定路径' (Save image to specified path) clearly states the action (save) and resource (image), making the purpose immediately understandable. It distinguishes from sibling tools like 'load_image' or 'convert_format' by focusing on saving rather than loading or converting. However, it doesn't explicitly differentiate from all siblings (e.g., 'create_gif' also saves output).

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an image source first), compare to similar tools like 'convert_format' (which might also save), or specify use cases (e.g., for final output vs. intermediate processing). Usage is implied but not articulated.

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