get_detection
Fetch a detection by UUID, polling until it completes (bounded). Use after detect_deepfake when a long job exceeded its wait budget.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | ||
| max_wait_seconds | No |
Resemble AI
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Mentions polling 'until it completes (bounded)' and parameter max_wait_seconds, but doesn't detail timeout behavior, errors, or idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Front-loads purpose and behavior, then usage context. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, description doesn't mention return value structure. Adequate for 2-param polling tool but missing details on response format and potential errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0% so description must compensate. Explains 'uuid' as identifier, implies max_wait_seconds as polling bound, but lacks explicit definition of max_wait_seconds beyond default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Fetch', resource 'detection by UUID', and polling behavior. Distinguishes from siblings by referencing usage after detect_deepfake.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use after detect_deepfake when a long job exceeded its wait budget', providing clear context. Lacks explicit when-not but implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.6/5.0
Disambiguation5/5
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.
Naming Consistency4/5
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.
Tool Count5/5
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.
Completeness4/5
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.