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get_recognition_group_image

Retrieve the reference crop image for a face or vehicle recognition group as base64-encoded JPEG data.

Instructions

Get a recognition group's reference crop. Returns base64-encoded JPEG image data.

host: console name, ID, or composite ID (MAC:numericId format). type: recognition type. Use 'face' or 'vehicle' (singular -- plural forms return HTTP 400 from upstream). Forwarded to the API as-is. group_id: the group's stable id, e.g. face_90.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostYes
typeYes
group_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the output format (base64 JPEG), that the type parameter is forwarded as-is upstream, and that plural type values return HTTP 400. It does not explicitly state read-only semantics, but the 'get' verb and nature of the operation make this reasonably clear.

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?

The description is compact and front-loaded with the core purpose, followed by one line per parameter. Every sentence adds value, and the format is scannable and easy for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, the description need not explain return values in detail, and it already mentions the base64 JPEG format. All three required parameters are described with formats and examples, making the tool fully callable without further research.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by explaining all three parameters: host formats, accepted type values, and group_id's stable id format with an example. This is exactly the kind of semantic enrichment needed beyond the bare schema.

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?

The description states a specific action ('Get a recognition group's reference crop') and a distinct resource, clearly distinguishing it from sibling tools like list_recognition_groups or get_recognition_group_counts. It also specifies the return type, leaving no ambiguity about what the tool does.

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

Usage Guidelines3/5

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

The description gives strong contextual guidance for parameters (which values are accepted, what causes HTTP 400), but it does not explicitly discuss when to choose this tool over alternatives such as get_thumbnail or list_recognition_detections. Usage is implied rather than contrasted with siblings.

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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