Get the audio source-tracing report for a detection (which AI platform generated the fake audio). Only available when detection ran with audio_source_tracing and labeled the audio fake.
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions the prerequisite condition and the nature of the report (which AI platform). However, it does not describe the output format, error behavior if condition not met, or any side effects, leaving 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 two sentences with no wasted words. The first sentence states the purpose, the second adds a crucial condition. It is front-loaded and concise.
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 tool with one parameter and no output schema, the description covers purpose and a key prerequisite. However, it lacks details on the report's contents (beyond platform) and error cases, making it somewhat 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?
The only parameter 'uuid' has no description in the schema (0% coverage). The description partially clarifies that it is for a detection, but does not explicitly state it is the detection's UUID or provide further formatting details, adding minimal 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 gets an audio source-tracing report for a detection, specifying which AI platform generated the fake audio. It distinguishes from siblings by focusing on source tracing and including a condition for availability.
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 says the report is only available when detection ran with audio_source_tracing and labeled the audio fake, providing clear context for use. It does not explicitly compare to alternatives but implies the condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: media analysis, watermarking, deepfake detection, watermark detection, detection retrieval, question answering about detections, and audio source tracing. No two tools overlap in functionality.
Tool names consistently use lowercase with underscores and a verb_noun pattern, though 'ask_about_detection' is slightly longer and deviates from the direct verb_noun structure (e.g., 'detect_deepfake'). Overall, the pattern is predictable.
Seven tools effectively cover the core capabilities of the Resemble AI server (media analysis, deepfake detection, watermarking, source tracing, and result queries). The count feels well-scoped for the domain.
The tool set covers the main workflows: detection, analysis, watermarking, and retrieval. Minor gaps exist, such as lacking a tool to list all detections or delete media/watermark, but the essential operations are present.