Magic Hour

face_detection_retrieve_details

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

Name	Required	Description	Default
`id`	Yes	The id of the task. This value is returned by the [face detection API](https://docs.magichour.ai/api-reference/files/face-detection#response-id).
`context`	No	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."

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	The id of the task. This value is returned by the [face detection API](https://docs.magichour.ai/api-reference/files/face-detection#response-id).
`faces`	Yes	The faces detected in the image or video. The list is populated as faces are detected.
`status`	Yes	The status of the detection.
`credits_charged`	Yes	The credits charged for the task.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 11, 2026

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"
+}

First observedSep 1, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the return content ('list of faces detected in the image or video'), which is genuine behavioral context, but says nothing about permissions, failure modes, or whether the task result is retained/expires. Adequate but incomplete for an unannotated tool.

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 action and followed by the use case. The inline documentation links add length but earn their place by pointing to the consuming swap APIs.

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?

With an output schema present, return values need not be explained, and the description covers purpose plus downstream integration. It stops short of clarifying the relationship to the detect_faces sibling, but is otherwise complete for a single-id retrieval tool.

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 description coverage is 100%, so both the id (with its provenance link to the face detection API) and the analytics-only context field are already documented in the schema. The description adds no parameter-level meaning beyond that, so baseline 3 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?

States a specific verb and resource ('Get the details of a face detection task'), which cleanly separates it from the sibling face_detection_detect_faces, which creates the task. It does not name that sibling explicitly, but the 'task details' framing makes the retrieval role clear.

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?

It explains the downstream purpose clearly ('to use in the face swap photo or face swap video API calls for multi-face swaps'), which implies why an agent would call it. However, it never states when to use it versus siblings like face_detection_detect_faces, nor any prerequisites (e.g., an id already returned from detection).

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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