Analyze media for structured intelligence: transcription, translation, language, speaker info, emotion, scene description, abnormalities, and misinformation analysis. media_type: auto | audio | video | image.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| media_type | No | auto | |
| structured_json | No | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden. It lists outputs but does not disclose potential side effects, authentication needs, or processing behavior (e.g., asynchronous nature, error handling), leaving some 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 concise (two sentences) with no filler, but the first sentence could be better structured (e.g., grouping related outputs). Still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and low parameter documentation make the description incomplete for an agent to fully understand tool behavior and response format, especially for a 4-param 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?
With 0% schema description coverage, the description only clarifies media_type (auto/audio/video/image) but does not explain the required 'url' parameter or optional 'structured_json' and 'max_wait_seconds', leaving most parameters undocumented.
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 analyzes media for structured intelligence and lists specific outputs (transcription, translation, etc.), distinguishing it from siblings that focus on watermarking and 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 implies use for general media analysis but lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives or prerequisites.
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.