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Get face detection details

face_detection_retrieve_details
Read-only

Get the details of a face detection task.

Use this API to get the list of faces detected in the image or video to use in the face swap photo or face swap video API calls for multi-face swaps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe id of the task. This value is returned by the [face detection API](https://docs.magichour.ai/api-reference/files/face-detection#response-id).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe id of the task. This value is returned by the [face detection API](https://docs.magichour.ai/api-reference/files/face-detection#response-id).
facesYesThe faces detected in the image or video. The list is populated as faces are detected.
statusYesThe status of the detection.
credits_chargedYesThe credits charged for the task.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / context
      Removed value: -{
      -  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
      -  "type": "string"
      -}
  2. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
      +  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
      +  "type": "string"
      +}
  3. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered externally. The description adds the downstream purpose (feeding multi-face swaps), which is useful but not a behavioral trait of the call itself; it says nothing about latency, pagination, or what constitutes a completed task vs a pending one.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose and followed by the practical downstream use. No redundant restatement of the title, though the second sentence is a bit long with two embedded links.

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?

An output schema exists, so the description need not explain return values, and the annotation block covers the read-only safety profile. The description supplies the caller's reason for retrieving details (multi-face swap inputs), which is the main thing the structured fields cannot convey. Minor gap: no mention of task state handling.

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 is 100% with a single required 'id' parameter whose description already explains it is returned by the face detection API. The description adds no syntax, format, or constraint beyond that, so the baseline of 3 for fully-covered params applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Get the details of a face detection task.' It is distinguishable from the sibling face_detection_detect_faces (which creates/initiates the detection) and from other *_retrieve_details tools by naming the face detection resource. It stops short of explicitly contrasting with the detect_faces sibling, so it earns a 4 rather than a 5.

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?

It gives clear usage context: use this to fetch the detected face list so it can be supplied to the face swap photo/video APIs for multi-face swaps. That tells the agent the workflow position (after detection, before swap). It does not explicitly state when-not to use it or name the alternative detect_faces tool, so it is 4 rather than 5.

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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