Ludo AI Game Assets

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 cost ('consumes 5 credits per call'). It lacks critical behavioral details: whether it's a read/write operation, expected latency, rate limits, authentication needs, or what happens on failure. The cost hint is minimal value against the missing context.

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 two short sentences, the description is under-specified and front-loaded with trivial information (name restatement and cost). It wastes space on tautology instead of delivering essential purpose or usage context, failing to earn its place effectively.

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

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

For a complex tool with 1 nested parameter (requestBody containing 16 properties), no annotations, and no output schema, the description is severely incomplete. It omits explanation of what the tool returns, error handling, and overall functionality, leaving significant gaps despite the detailed input schema.

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. The description adds no parameter semantics beyond what's in the schema, meeting the baseline score of 3 where the schema does the heavy lifting without extra description 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 'animateSprite. This endpoint consumes 5 credits per call.' is a tautology that merely restates the tool name and adds cost information. It fails to specify what the tool actually does (e.g., generate animated sprites from static images with motion prompts), nor does it differentiate from siblings like 'createImage' or 'transferMotion'.

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 does not mention prerequisites, context for animation generation, or comparisons to sibling tools like 'createVideo' or 'generateWithStyle', leaving the agent with no usage direction.

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 only discloses credit consumption (3 per call), which is useful but insufficient. It doesn't cover permissions, rate limits, whether it's async/sync, what happens on failure, or output behavior. For a tool with complex inputs and no output schema, this leaves critical 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?

Two sentences with zero waste, but under-specified. The first sentence is tautological, and the second adds useful credit info. However, it's too brief for a tool with 5 parameters and no annotations, missing essential context that would justify its 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?

Inadequate for a complex tool with 5 parameters, no annotations, and no output schema. The description doesn't explain the tool's function, usage, behavioral traits, or parameters. It fails to provide enough context for an agent to understand how to invoke it correctly or what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds no parameter information beyond what's in the schema. The schema details parameters like 'image', 'request_id', 'texture_size', etc., but the description doesn't explain their purpose, relationships, or how they affect the 3D model creation.

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 'create3DModel. This endpoint consumes 3 credits per call.' is tautological - it restates the tool name without explaining what it does. It mentions credit consumption but doesn't specify the action (e.g., converting an image to a 3D model). Compared to siblings like 'createImage' or 'createVideo', it doesn't distinguish its specific function.

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. It doesn't mention prerequisites, context, or compare to sibling tools like 'get3DModelResults' (which might retrieve results). The credit cost is noted but doesn't inform usage decisions relative to other 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions credit consumption (0.5 credits per result), which is useful but insufficient. It lacks critical details such as whether this is a generative/mutation operation, expected latency, rate limits, authentication requirements, or output format (e.g., image URLs or data).

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 only two short sentences, but this brevity comes at the cost of under-specification. While there is no wasted text, the first sentence is redundant, and the second only covers cost. It is front-loaded but lacks substantive content.

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 (generative image creation with multiple parameters), lack of annotations, and no output schema, the description is severely incomplete. It fails to explain the tool's purpose, usage, behavior, or output, leaving the agent with inadequate information to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters (e.g., prompt, image_type, n). The description adds no parameter-specific information beyond what the schema provides, meeting the baseline score of 3 for high schema coverage without additional 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 'createImage. This endpoint consumes 0.5 credits per result.' is a tautology that merely restates the tool name followed by cost information. It does not specify what the tool actually does (e.g., generate images from text prompts) or distinguish it from sibling tools like editImage or getImageResults.

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 does not mention any context, prerequisites, or comparisons to sibling tools such as editImage (for modifications) or getImageResults (for retrieving results).

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 only mentions the credit cost (4 credits per call), which is useful operational information. However, it fails to describe what the tool actually does behaviorally - whether it generates music synchronously or asynchronously, what format the output takes, whether it's a creation or modification operation, or any other behavioral characteristics.

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 two short phrases) but this brevity comes at the cost of meaningful information. While it's not verbose, it's under-specified rather than efficiently informative. The structure is simple but doesn't front-load the most important information about what the tool actually does.

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 this is a creation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool creates, how it works, what the output looks like, or any behavioral characteristics beyond the credit cost. For a tool that presumably generates music from text descriptions, this leaves critical gaps in 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 fully documents the single parameter (requestBody) and its nested properties. The description adds no 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 is essentially tautological - it restates the tool name 'createMusic' without explaining what the tool actually does. While the name itself suggests music creation, the description fails to specify what kind of music creation (generation from text, editing existing tracks, etc.) or what resources are involved. It doesn't distinguish this from sibling tools like createSoundEffect or createSpeech.

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

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

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any prerequisites, context for usage, or comparison to sibling tools like createSoundEffect or createSpeech. The credit cost information is operational but doesn't help an agent decide when this specific tool is 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions the credit cost (2 credits per call), which is useful operational context. However, it fails to describe important behavioral aspects: that this is an asynchronous generation tool (implied by request_id parameter), that results must be retrieved separately via another endpoint, what the output format is, or any rate limits beyond the credit cost.

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 two short sentences. However, it's under-specified rather than efficiently informative. The first sentence is a tautology, and while the second sentence provides useful operational information (credit cost), the overall description lacks essential purpose information that should be front-loaded.

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

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

For a creative generation tool with no annotations and no output schema, the description is incomplete. It mentions credit cost but fails to explain the asynchronous nature (implied by request_id), how to retrieve results, what the output format is, or typical use cases. Given the complexity of sound generation and the lack of structured metadata, the description should provide more contextual information.

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 parameter information beyond what's in the schema. The baseline score of 3 is appropriate since the schema does the heavy lifting, though the description provides zero additional 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 is essentially a tautology - it restates the tool name 'createSoundEffect' without adding meaningful context about what the tool actually does. While it mentions 'This endpoint consumes 2 credits per call', this doesn't explain the tool's purpose. The description fails to specify that this generates audio from text descriptions or distinguish it from sibling audio tools like createMusic or createSpeech.

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

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

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any specific use cases, prerequisites, or comparisons to sibling tools like createMusic (for musical compositions) or createSpeech (for speech generation). The credit cost mention doesn't constitute usage guidance.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states credit consumption (1 per call), which is useful but insufficient. It lacks details on permissions, rate limits, output format (e.g., audio file), processing time, or error handling. For a tool that likely generates audio, this leaves critical behavioral traits unexplained.

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 (one sentence) and front-loaded, with no wasted words. However, it under-specifies the tool's purpose, making it less effective despite its brevity. It earns a 4 for structure but loses points for lacking essential content.

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 (voice cloning, text-to-speech) and lack of annotations and output schema, the description is incomplete. It fails to explain what the tool returns (e.g., audio data or a job ID), how to handle results, or any behavioral nuances. The credit cost is noted, but other critical context is missing 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?

Schema description coverage is 100%, providing clear documentation for parameters like text, sample, and request_id. The description adds no parameter-specific information beyond what the schema already covers. According to rules, with high schema coverage (>80%), the baseline score is 3 even without param 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 'createSpeech. This endpoint consumes 1 credits per call.' is tautological (restates the name) and only adds billing information. It does not specify what the tool actually does (e.g., text-to-speech generation with voice cloning), nor does it distinguish from siblings like createSpeechPreset or createVoice. The purpose remains vague 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?

No guidance is provided on when to use this tool versus alternatives. It does not mention siblings like createSpeechPreset or createVoice, nor does it specify prerequisites, contexts, or exclusions. The agent must infer usage from the name and schema 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 for behavioral disclosure. It mentions credit consumption (1 per call), which is useful operational context. However, it doesn't describe what the tool actually creates (audio file? preset configuration?), whether it's synchronous or asynchronous, error conditions, or what happens on success. For a creation 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 (two short sentences) but under-specified rather than efficiently informative. While it doesn't waste words, it also fails to provide essential information about the tool's purpose and behavior. The credit cost information is front-loaded but insufficient as primary content.

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

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

For a creation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what gets created, how results are retrieved (though request_id hints at async retrieval), or what the output looks like. The credit cost is helpful but doesn't compensate for missing core behavioral information needed for proper tool invocation.

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 parameter information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation coming entirely from the schema, not from the description itself.

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 'createSpeechPreset. This endpoint consumes 1 credits per call.' is tautological - it restates the tool name without explaining what it actually does. It doesn't specify what resource is being created (a speech preset? audio file from preset?) or distinguish it from sibling tools like 'createSpeech' or 'createVoice'. The credit consumption note is operational but doesn't clarify purpose.

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 like 'createSpeech' or 'createVoice'. The description provides only credit cost information, which doesn't help an agent decide when this specific tool is appropriate versus other text-to-speech or audio generation 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully reveals the credit consumption system (5-15 credits depending on duration), which is important operational context not captured elsewhere. However, it fails to describe what the tool actually returns (video generation results), error conditions, or any rate limits beyond the credit system.

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 sentence that efficiently communicates the credit consumption model. However, it's under-specified for a tool with this complexity, missing the core purpose statement. The structure is front-loaded with operational cost information but lacks the fundamental 'what this tool does' explanation.

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

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

For a video generation tool with no annotations and no output schema, the description is severely incomplete. It fails to explain what the tool actually produces, how results are retrieved (only hinting at a 'results endpoint'), or any behavioral characteristics beyond credit costs. The absence of output information is particularly problematic given the tool's generative nature.

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, the input schema comprehensively documents all parameters. The description adds no parameter-specific information beyond the credit consumption details that correlate with the 'duration' parameter. This meets the baseline expectation when schema coverage is complete, but adds minimal additional semantic 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 'createVideo' is a tautology that merely restates the tool name without specifying what the tool actually does. It fails to mention that this tool generates videos from source images using motion prompts, which is critical context missing from the name alone. No differentiation from sibling tools like 'animateSprite' or 'transferMotion' is provided.

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 'animateSprite' or 'createImage'. While it mentions credit consumption based on duration, this is operational cost information rather than usage context. There are no explicit when/when-not instructions or references to sibling tools for comparison.

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 only states credit consumption (1 per call), which is useful but insufficient. It doesn't disclose behavioral traits like whether it's a read/write operation, latency, rate limits, authentication needs, or what happens upon creation (e.g., returns a voice file or ID). For a creation 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?

Two sentences: one tautological and one about credits. It's brief but under-specified—the first sentence wastes space restating the name, and the second, while useful, doesn't cover core functionality. It's not front-loaded with purpose, reducing effectiveness.

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 no annotations, no output schema, and a creation tool with nested parameters, the description is incomplete. It lacks details on what the tool returns (e.g., voice sample, ID), error handling, or integration with sibling tools like 'getAudioResults'. The credit note is helpful but doesn't suffice for a tool with behavioral complexity.

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 (e.g., 'voice_description', 'text', 'type'). The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting, but the description doesn't compensate or add meaning.

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 'createVoice. This endpoint consumes 1 credits per call.' is tautological as it restates the tool name 'createVoice' without explaining what it does. It mentions credit consumption but not the core function of generating a voice from a description. Compared to siblings like 'createSpeech' or 'createSoundEffect', it doesn't distinguish its specific purpose.

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 like 'createSpeech' or 'createSoundEffect'. The credit consumption note is operational but doesn't help the agent decide between similar tools. There's no mention of prerequisites, context, 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 only mentions credit consumption (0.5 credits per result), which is useful but insufficient. It lacks critical information such as whether this is a read-only or destructive operation, authentication requirements, rate limits, or what the tool returns. The description fails to adequately describe behavioral traits beyond the minimal credit cost.

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—only one sentence—with no wasted words. However, it's under-specified rather than efficiently informative, as it lacks essential details about the tool's purpose and usage. It's front-loaded but incomplete.

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 an image editing tool with 6 parameters (nested in requestBody), no annotations, and no output schema, the description is incomplete. It fails to explain what the tool does, when to use it, or what it returns, leaving significant gaps for an AI agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description adds no additional semantic information about parameters 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 parameter 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 is essentially a tautology that restates the tool name 'editImage' without specifying what the tool actually does. It mentions credit consumption but provides no information about the action performed (e.g., editing images based on text prompts). This fails to distinguish it from sibling tools like 'createImage' or 'removeBackground'.

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?

There are no usage guidelines provided. The description doesn't indicate when to use this tool versus alternatives like 'createImage' or 'removeBackground', nor does it mention any prerequisites or contextual cues for invocation.

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 discloses credit consumption (0.5 credits per result). It misses critical behavioral traits: mutation nature (creates new poses), response format, processing time, error conditions, or rate limits. The single piece of information is insufficient 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 just one sentence, but it's under-specified rather than efficiently informative. It wastes the opportunity to state the tool's purpose, making it less helpful despite 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?

For a mutation tool with no annotations and no output schema, the description is severely incomplete. It lacks purpose, usage context, behavioral details, and output information, relying entirely on the input schema which doesn't cover these aspects.

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 parameter information beyond what's already in the schema, meeting the baseline score of 3 where 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 is tautological, merely restating the tool name 'generatePose' without specifying what it actually does. It fails to mention that it generates new poses for sprite images, which is only revealed in the input schema's description. No differentiation from siblings is provided.

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 given. The description only mentions credit consumption, which doesn't help in selecting this tool over sibling tools like 'animateSprite' or 'transferMotion'. Usage context is completely absent.

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 do so. It only states credit consumption (0.5 per result), omitting critical details such as whether this is a read/write operation, rate limits, authentication needs, or what the tool actually does (e.g., generates images). This is inadequate for a tool with complex parameters.

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 under-specified rather than concise—it's two short sentences but wastes space on tautology ('generateWithStyle.') and omits essential purpose and usage details. Every sentence should earn its place, but here they provide minimal value, making it inefficient.

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 (multiple parameters, no annotations, no output schema), the description is severely incomplete. It lacks purpose, behavioral context, and output information, failing to compensate for the absence of structured data. This leaves the agent unable to understand or use the tool effectively.

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 (e.g., 'style_image', 'prompt', 'image_type'). The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 since the schema handles 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 'generateWithStyle. This endpoint consumes 0.5 credits per result.' is tautological (restates the tool name) and only adds cost information. It doesn't specify what the tool actually does (e.g., generate images with style transfer from a reference image), making it vague and unhelpful for distinguishing from siblings like 'createImage' or 'editImage'.

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 mentions credit consumption but doesn't explain the tool's purpose, context, or prerequisites, leaving the agent with no basis for selection among sibling tools like 'createImage' or 'editImage'.

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 disclosure burden. It fails to explain what 'recent' means (pagination window?), whether this operation is idempotent, what fields are returned, or typical latency considerations. 'Retrieves' implies read-only safety, but this is never explicitly guaranteed.

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?

Single sentence with no redundancy and clear front-loading. However, given the lack of annotations and output schema, this level of brevity is insufficient; the description is concise but under-specified rather than efficiently informative.

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?

Critical gaps remain: no output schema means the return structure is undefined, and the description doesn't compensate by describing result format or status states. The async workflow relationship (crucial for 3D generation which is typically long-running) is unexplained, and 'recent' lacks quantification.

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 coverage, the baseline is 3. The description provides no additional semantic context for 'request_id' (e.g., 'obtained from create3DModel response') or explain the behavior when omitted (returns all recent results). It relies entirely on the schema's 'Filter results by request_id' text.

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 uses the specific verb 'Retrieves' and clearly identifies the resource as 'API-generated 3D model results', distinguishing it from siblings like getImageResults or getVideoResults. However, it loses a point for vagueness around 'recent' (time-based? last N?) and not explicitly clarifying this polls results from create3DModel.

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 explicit guidance on when to use this tool versus alternatives, or how it relates to the create3DModel workflow. While the sibling tools imply an async pattern (create then get results), the description doesn't state to 'use this after create3DModel to poll for completion' or warn against calling it before job submission.

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. While 'Retrieves' implies read-only access, the description fails to disclose what 'recent' means (retention policy), whether results are removed after retrieval, pagination behavior, or the expected response format when request_id is provided versus omitted.

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?

Extremely concise at 5 words. Front-loaded with verb first. No wasted words or redundancy. However, brevity comes at the cost of missing contextual information, though that falls under completeness rather than 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?

For a simple retrieval tool with one optional parameter and no output schema, the description is minimally viable but incomplete. It omits the relationship to creation tools (createMusic, etc.), lacks return value description, and does not explain the polling pattern implied by the tool name and 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?

Input schema has 100% coverage with the single parameter 'request_id' fully documented as 'Filter results by request_id'. The description adds no semantic detail beyond the schema, but baseline 3 applies due to complete 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?

Description uses specific verb 'Retrieves' and identifies resource 'API-generated audio results'. The term 'Audio' effectively distinguishes this from sibling result-fetching tools like getImageResults, getVideoResults, and get3DModelResults. However, it does not clarify what 'API-generated' refers to (i.e., results from createMusic/createSpeech siblings).

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 provided on when to invoke this tool versus alternatives, or that it should be used to poll for results after calling the createMusic/createSoundEffect/createSpeech/createVoice tools. The mention of 'recent' implies a time window but offers no specifics on retention periods.

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 'recent' implying time-window limits but fails to disclose whether results are ephemeral, how many are returned, if this is safe to poll repeatedly, or what structure the response takes.

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?

Single sentence of four words is efficiently structured but crosses into underspecification. For an async polling tool, this brevity sacrifices necessary context about the creation-retrieval workflow and polling semantics.

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?

Missing critical context expected for an async result-retrieval tool: relationship to createImage, polling behavior, output format/structure, and the meaning of 'recent'. No output schema exists to compensate, leaving the agent unaware of what constitutes a result object.

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 has 100% description coverage for the single optional parameter, establishing baseline 3. The description adds no semantic detail about the request_id (e.g., that it comes from createImage) or what happens when omitted (returns all recent?), but doesn't need to compensate for schema 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?

Clear verb 'Retrieves' and specific resource 'API-generated image results' that distinguishes from sibling video/audio/3D result tools. However, 'recent' lacks temporal specificity and the async nature (polling for createImage jobs) is implied rather than 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?

No explicit guidance on when to use versus alternatives, or that this tool is designed for polling async generation results from createImage. The phrase 'API-generated' hints at the source but fails to describe the workflow prerequisite (needing a request_id from a prior creation job).

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 only indicates this is a read operation ('Retrieves') but fails to clarify what 'recent' means (time window), whether results are deleted after retrieval, pagination behavior, or rate limiting considerations.

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, front-loaded sentence with no redundancy. While appropriately concise, it may be overly minimal given the complete absence of annotations and output schema—leaving too much unsaid for a tool that likely implements an async job retrieval pattern.

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?

Inadequate for a result-retrieval tool with no annotations or output schema. The description fails to explain the relationship with animateSprite (the presumed generation tool), doesn't define the 'recent' time window, and omits expected return structure or status codes (pending vs completed spritesheets).

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 coverage is 100% (the single optional parameter request_id is fully documented in the schema as 'Filter results by request_id'). The description adds no parameter-specific context, but with complete schema coverage, this meets the baseline expectation.

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 uses the specific verb 'Retrieves' and identifies the resource as 'API-generated spritesheet results', distinguishing it from sibling result-fetching tools like getImageResults or getVideoResults. However, it assumes familiarity with what distinguishes a 'spritesheet' from a regular image in this API 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?

No guidance provided on when to use this tool versus alternatives (e.g., getImageResults), nor does it mention the prerequisite relationship with animateSprite (the likely sibling that generates these results). The word 'recent' implies temporal filtering but doesn't explain the retrieval pattern.

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 offers minimal detail. It mentions 'recent' results without defining the time window, does not clarify pagination behavior, and omits whether this is a blocking or polling operation, all critical for an asynchronous result-fetching 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?

At four words, the description is extremely terse and front-loaded, but underspecified for a tool participating in a complex async workflow. While not wasteful, it sacrifices necessary behavioral context for 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 appears to be part of an asynchronous job system (implied by siblings like createVideo and get...Results pattern), the description inadequately explains the job lifecycle, polling mechanisms, or return value structure. For a result-fetching tool with no output schema, it should describe what 'results' contain.

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 for the single optional parameter (request_id), the schema sufficiently documents inputs. The description adds no parameter-specific guidance, but baseline 3 is appropriate when the schema carries the full load.

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 (Retrieves) and resource (API-generated video results), distinguishing it from sibling creation tools like createVideo. However, it does not explicitly differentiate from other result-retrieval siblings like getImageResults or getAudioResults, though the name makes this implicit.

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, nor does it explain the typical workflow (e.g., that one likely calls createVideo first to generate a request_id, then polls this endpoint). Critical context for an async result-fetching pattern is missing.

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 limited behavioral insight. It mentions the presets are 'available' and for use with another endpoint, but doesn't disclose traits like whether it's read-only, requires authentication, has rate limits, or what the return format looks like. This leaves significant gaps 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 function and context 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's simplicity (0 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic purpose and links to a sibling tool, but lacks details on behavior, output format, or error handling. For a low-complexity tool, this is adequate but leaves room for improvement in 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 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 does mention the context ('for use with the transferMotion endpoint'), which provides some value. Baseline for 0 params is 4, as it adequately handles the absence of 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 clearly states the tool's purpose with a specific verb ('Lists') and resource ('animation presets'), and mentions their intended use ('for use with the transferMotion endpoint'). However, it doesn't explicitly differentiate from sibling tools like 'createSpeechPreset' or 'getVideoResults', 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 minimal guidance by linking the presets to 'transferMotion endpoint', implying usage context. However, it lacks explicit when-to-use rules, alternatives (e.g., vs. creating presets), or exclusions, offering only implied usage without clear directives.

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 only mentions credit consumption (0.5 credits per result) but doesn't describe what the tool actually does, what permissions are needed, rate limits, error conditions, or what the output looks like. This is inadequate for a tool that presumably performs image 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?

While technically concise (one sentence), the description is under-specified rather than efficiently informative. It wastes the opportunity to explain the tool's purpose and instead focuses only on credit consumption, which doesn't help an agent understand what the tool does.

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

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

For an image processing tool with no annotations and no output schema, the description is severely incomplete. It doesn't explain what the tool does, what it returns, or how it differs from sibling image tools. The credit information is the only contextual detail provided.

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 parameter information beyond what's in the schema. The baseline of 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 is essentially a tautology - it restates the tool name 'removeBackground' without explaining what the tool actually does. It doesn't specify what resource is being acted upon (images) or what the outcome is (background removal). The credit consumption information doesn't clarify the purpose.

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

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

No guidance is provided about when to use this tool versus the many sibling tools (like editImage, createImage, etc.). The description doesn't mention prerequisites, appropriate contexts, or alternatives for background removal tasks.

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 minimal information. It only mentions credit consumption (8 credits per call) without explaining what the tool actually does, what permissions are needed, rate limits beyond credit cost, or what happens to input data. The core functionality must be inferred from the input schema.

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 (two short phrases), the description is under-specified rather than efficiently informative. The first phrase is a tautology, and the second only mentions credit cost. It fails to front-load the most important information about what the tool does.

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

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

For a complex tool with 13 parameters, no annotations, and no output schema, the description is severely inadequate. It doesn't explain the tool's purpose, when to use it, what it returns, or any behavioral characteristics beyond credit cost. Users must rely entirely on the input schema to understand this tool.

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 comprehensively documents all parameters. The tool description adds no parameter information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information 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 is a tautology that merely repeats the tool name ('transferMotion') and adds a credit cost statement. It fails to explain what the tool actually does - transferring motion from a video onto a static image to create an animated spritesheet. This information is only found in the input schema description, not in the main tool description.

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

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

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any of the sibling tools (like animateSprite or createVideo) or explain this tool's specific use case. The credit cost mention doesn't constitute usage guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the HTTP response codes (200 for valid, 403 for invalid), which is useful, but lacks other critical behavioral details such as authentication requirements, rate limits, error handling beyond these codes, or whether this is a read-only operation. The description does not contradict any annotations since none exist.

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 two sentences that directly state the tool's purpose and outcome. Every word earns its place, and it's front-loaded with the core functionality, making it efficient and easy to parse.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does and the basic return codes, but for a validation tool, it lacks details on error conditions beyond 403, performance characteristics, or integration context, which could help an agent use it more effectively.

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 0 parameters with 100% coverage, so the description doesn't need to compensate for missing parameter documentation. The description adds no parameter-specific information, which is appropriate here, but it does provide context about the tool's function that goes beyond the empty 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 clearly states the specific action ('Validates an API key') and the resource involved ('API key'), making the purpose immediately understandable. It distinguishes this tool from all sibling tools, which are focused on media creation/editing tasks, by being the only one handling API key validation.

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 or in what context it should be invoked. It doesn't mention prerequisites, dependencies, or typical scenarios where API key validation would be needed, leaving the agent with minimal usage direction.

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