Resemble AI

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

With no annotations provided, the description must fully disclose behavior. It does not mention whether the tool is synchronous or asynchronous, potential side effects, rate limits, or what happens during the max_wait_seconds period. The mention of 'max_wait_seconds' hints at a potential timeout but is not explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single sentence that efficiently conveys the tool's purpose. It is concise and front-loaded with key capabilities, but could benefit from bullet points or more structure to improve readability.

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 4 parameters, no output schema, and no annotations, the description is incomplete. It lacks explanations for 'structured_json' and 'max_wait_seconds', does not describe the response format, and omits behavioral details.

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 only adds meaning for the 'media_type' parameter by listing possible values. Parameters 'url', 'structured_json', and 'max_wait_seconds' are not explained. Since schema coverage is 0%, the description should provide more context for all 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 it analyzes media for structured intelligence and lists specific capabilities (transcription, translation, language, speaker info, emotion, scene description, abnormalities, misinformation). It also mentions media_type options, distinguishing it from sibling tools focused on watermarking or deepfake detection.

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, no prerequisites, no when-not-to-use conditions, and no explicit context for choosing between media 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 must fully disclose behavior. It states the watermark is invisible and returns a URL, but omits whether it modifies the original, waits synchronously (max_wait_seconds), or handles errors. The term 'provenance watermark' is not explained. The description is insufficient for a tool with no annotations.

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 two sentences, front-loading the core action. It is concise with no wasted words. However, it could be improved by adding brief parameter explanations 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 4 parameters, no output schema, and no annotations, the description is incomplete. It does not explain custom_message or max_wait_seconds, nor the return format or error conditions. For a tool with sibling detection tools, more context is needed for correct selection and 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 0%, so the description must compensate. It only adds value for the strength parameter by noting it applies to image/video only. The url, custom_message, and max_wait_seconds parameters are not described. custom_message's purpose is unclear, and max_wait_seconds suggests an async behavior not mentioned. The description's contribution is minimal.

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 'embed' and resource 'watermark', clearly stating it embeds an invisible provenance watermark into media and returns the URL. It mentions 'audio-first' which hints at primary use case, and the strength parameter for image/video. This distinguishes it from sibling tools that focus on detection and analysis.

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

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

The description implies usage for applying watermarks but does not explicitly state when to use it versus alternative tools. It mentions strength only applies to image/video, but lacks guidance on prerequisites or scenarios. The sibling tools are about detection, so the purpose is clear but not elaborated.

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 must disclose behavioral traits. It only states it 'returns the grounded answer' without mentioning latency (though parameter max_wait_seconds hints at it), permission requirements, or error handling. The description is too minimal for a tool without annotation support.

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, well-structured sentence that front-loads the core purpose. It contains no superfluous information and efficiently conveys the tool's 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 3 parameters, no output schema, and no annotations, the description is too sparse. It omits details on return format, timeout behavior, error conditions, and preconditions, making it incomplete for an AI agent to reliably invoke.

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 only indirectly describes 'query' via example and mentions 'detect_uuid' implicitly, but fails to explain 'max_wait_seconds' or parameter format. The description adds minimal value beyond the schema structure.

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 verb (ask), resource (completed detection), and provides an example query. It distinguishes from siblings like 'get_detection' which likely retrieves metadata, while this tool answers natural-language questions about the detection.

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 specifies that the tool is for 'COMPLETED detection,' providing clear context on when to use it. However, it does not explicitly exclude other tools or mention alternatives, but the sibling tool names imply their purposes.

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?

Without annotations, the description adequately discloses polling behavior, timeout via max_wait_seconds, optional flags, and zero-retention mode. It does not hide destructive or mutating aspects, as it's a read-like detection 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 well-structured with a clear purpose first, followed by polling behavior and optional flags. It is slightly verbose but avoids redundancy and front-loads critical 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 9 parameters and no output schema, the description covers the tool's core behavior and most optional features. It lacks details on return structure, error handling, or media format constraints, but is sufficient for basic usage.

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 0% schema coverage, the description explains the main optional flags (media intelligence, audio tracing, visualization, reverse search, OOD detection, zero-retention) and the model_type enum. However, not all parameters (e.g., max_wait_seconds) are detailed beyond defaults.

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 detects deepfakes in audio, image, or video from a public HTTPS URL. It specifies the action (detect) and resource (media at URL), and the polling behavior distinguishes it from non-polling tools like get_detection.

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 implies polling to completion as a key behavior but does not explicitly state when to use this tool versus siblings like get_detection for existing results or trace_audio_source for audio-specific tracing. No exclusion criteria are given.

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 must carry the full burden of behavioral disclosure. It only states the primary action ('Check') and the audio-first nature, but does not mention whether the tool is read-only, requires authentication, has rate limits, or other behavioral traits. The description is minimal beyond purpose.

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, well-structured sentence that front-loads the core action. Every phrase adds value: 'public HTTPS URL', 'Resemble invisible watermark', and the parenthetical specifying audio-first behavior. No unnecessary 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 the tool's simplicity (one parameter, no output schema, no nested objects), the description covers the essential aspects: what it checks, the type of URL, the watermark type, and the audio-focused nature. It hints at the return format ('per-channel verdict'), but could be more explicit about the output structure or error conditions.

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 a single parameter and 0% schema description coverage, the description compensates by specifying that the URL must be 'public HTTPS', adding valuable semantic constraint beyond the schema's type string. This clarifies the acceptable format and reduces ambiguity.

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 a specific verb ('Check') and resource ('media at a public HTTPS URL'), clearly identifying the tool's function: detecting a 'Resemble invisible watermark'. The mention of 'audio-first; per-channel verdict for audio' further specifies scope, distinguishing it from sibling tools like 'detect_deepfake' or 'analyze_media'.

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 implies the tool should be used when you have a public HTTPS URL to check for a Resemble invisible watermark, but it does not explicitly state when not to use it or suggest alternatives among siblings. No comparative guidance 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?

Without annotations, the description carries the burden. It discloses polling behavior ('polling until it completes (bounded)') which is key. However, it doesn't specify what 'bounded' means or behavior on timeout/error. Still, it adds significant transparency beyond the bare minimum.

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 concise sentences. The first states the action and behavior, the second provides usage context. No wasted words; each sentence 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?

For a simple 2-parameter tool with no output schema, the description covers purpose, when to use, and basic behavior. However, it lacks details on return format, possible errors, or exact polling behavior (e.g., rate limits, timeout handling). Adequate but not 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?

Schema description coverage is 0%, so the description must compensate. It mentions UUID implicitly but does not explain the 'uuid' parameter format or the 'max_wait_seconds' parameter (default 60). The term 'bounded' hints at max_wait but not explicitly. Minimal added 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 explicitly states 'Fetch a detection by UUID' (verb + resource) and distinguishes it from 'detect_deepfake' by adding polling behavior. It clearly differentiates from siblings like 'detect_deepfake' which initiates the job.

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

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

The description provides explicit guidance: 'Use after detect_deepfake when a long job exceeded its wait budget.' This tells the agent exactly when to use this tool versus alternatives, covering both context and exclusion.

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

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

With no annotations provided, the description carries the full burden. It discloses the key behavioral trait (conditional availability) but does not mention whether the operation is read-only, its latency, or what the report contains beyond 'which AI platform generated the fake audio.' More behavioral details would improve 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 consists of two concise sentences. The first sentence front-loads the purpose. Every word earns its place with no redundancy.

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 only one parameter and no output schema, the description covers the essential purpose and usage condition. It briefly describes the output as a report identifying the AI platform, which is adequate for a simple tool. However, it could be more complete by noting the response format (e.g., text, JSON).

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 sole parameter 'uuid' is required and the description implies it is a detection UUID by stating 'for a detection.' However, with 0% schema description coverage, the description does not specify the format or how to obtain the UUID. It provides minimal additional meaning beyond the schema's type definition.

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 a specific verb ('Get') and identifies the resource ('audio source-tracing report'). It explains the tool's purpose (identifying which AI platform generated the fake audio), which distinguishes it from sibling tools like 'detect_deepfake' that only detect fakes.

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 explicitly states the prerequisite condition: 'Only available when detection ran with audio_source_tracing and labeled the audio fake.' This provides clear guidance on when to use the tool, though it does not mention alternatives or when not to use it.

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