Ask a natural-language question about a COMPLETED detection (e.g. 'how confident is the model that this is fake?'). Returns the grounded answer.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| detect_uuid | Yes | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full burden for behavioral disclosure. It fails to mention key traits: side effects (none expected, but not stated), authentication needs, rate limits, or what happens on timeout (max_wait_seconds). The constraint 'completed detection' is useful but 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 a single sentence with an example, which is concise and front-loaded. However, it lacks structure (e.g., broken into purpose, input, output) and the example is embedded rather than separated. It earns its place but could be more organized.
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 brief. It does not explain the return format (e.g., plain text, structured JSON), how 'grounded answer' relates to the detection data, or how max_wait_seconds affects the call. A more complete description would include these 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?
With 0% schema description coverage, the description must explain all parameters. It covers detect_uuid and query implicitly via the example and phrasing, but completely omits max_wait_seconds—its meaning, default, and behavior. The example provides some context for query format, but not for detect_uuid format.
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: ask a natural-language question about a completed detection, with an example query. It distinguishes from sibling tools like detect_deepfake or get_detection by specifying the interaction type (question-answering) and input requirement (completed 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 usage context ('completed detection') but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives among siblings (e.g., analyze_media, get_detection) or prerequisites like detection completion status.
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.