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IvedaAI: FaceCategory

ivedaai_face_category
Destructive

List, create, update, and delete face categories in IvedaAI to organize and manage groups of faces for video analytics.

Instructions

IvedaAI API — FaceCategory operations. Response JSON: "status" is the HTTP status code; "truncated"/"timedOut" flag a cut-off response.

GET /api/face/categories — List face categories

POST /api/face/categories — Create face category body: { colorCode?:string, name*:string }

DELETE /api/face/categories/{faceCategoryId} — Delete face category path: faceCategoryId*:string

PUT /api/face/categories/{faceCategoryId} — Update face category path: faceCategoryId*:string body: { colorCode?:string, name*:string }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoJSON request body, or form field values when uploading a file.
fileNoLocal file to upload, for operations that accept a file.
pathNoPath parameters, e.g. { "cameraId": 12 }
queryNoQuery string parameters for this operation.
operationYesWhich API operation to call, from the list in this tool's description.
Behavior3/5

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

Annotations already flag destructiveHint=true, so the description builds on that by adding response-behavior details ('truncated'/'timedOut') and indicating required fields. It does not contradict annotations, but beyond the response note it provides limited additional behavioral context such as auth needs or side effects.

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 well-structured with a front-loaded response note, followed by a consistent list of operations, paths, and parameter details. Every line is needed and adds value; there is no fluff or redundancy.

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

Completeness4/5

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

The description covers all operations, parameters, and required fields, and mentions response fields. While it lacks examples or explicit error handling, the combination of annotations, schema, and description is sufficient for an agent to select and invoke the tool correctly for basic CRUD operations.

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?

The input schema only provides generic container fields (body, path, query), but the description specifies exact parameters for each operation, including required vs optional fields (e.g., name*, colorCode?, faceCategoryId). This adds critical semantic meaning beyond the schema, compensating for the schema's generic nature.

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 clearly identifies the tool as managing FaceCategory resources and lists four explicit HTTP operations (GET, POST, DELETE, PUT) with their paths. This distinguishes it from sibling tools that target other resources. The verb+resource pairing is specific and unambiguous.

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 implies usage by listing the CRUD operations, but it does not explicitly state when to use this tool over alternatives. There is no mention of when not to use it, prerequisites, or comparisons with sibling tools. Guidance is inferred from the resource name rather than explicitly provided.

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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