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Detect faces

detect_faces

Starts an asynchronous face-detection task for a supplied image or video and returns its id and charge information. Use before a selected-face swap, then retrieve its status and detected faces with get_face_detection_results. Current API documentation describes detection as free; pricing may change.

Input Schema

NameRequiredDescriptionDefault
assetsYesProvide the assets for face detection
confidence_scoreNoConfidence threshold for filtering detected faces. * Higher values (e.g., 0.9) include only faces detected with high certainty, reducing false positives. * Lower values (e.g., 0.3) include more faces, but may increase the chance of incorrect detections.

Output Schema

NameRequiredDescriptionDefault
idYesThe id of the task. Use this value in the [get face detection details API](https://docs.magichour.ai/api-reference/files/get-face-detection-details) to get the details of the face detection task.
credits_chargedYesThe credits charged for the task.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

The description adds meaningful behavior beyond the annotations: the task is asynchronous, returns an id and charge information, and currently has a free pricing caveat that may change. It does not cover auth or rate limits, but with annotations declaring non-destructive and open-world behavior, this is sufficiently transparent.

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 three short sentences, front-loads the core operation, and includes only relevant context about async behavior, next-step retrieval, and pricing. There is no wasted wording.

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 the output schema, full input-schema coverage, and annotations, the description provides the key flow an agent needs: this is an async task, results are retrieved with get_face_detection_results, and charge information is returned. Nothing essential for correct invocation is missing.

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 the schema already fully documents the nested asset path and confidence_score parameters. The description only implies image or video input and does not add parameter-level detail beyond the schema, making the baseline score of 3 appropriate.

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 verb and resource: it starts an asynchronous face-detection task for an image or video and returns an id plus charge information. It also differentiates the tool from its retrieval sibling, get_face_detection_results, so an agent can identify its role without opening the schema.

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?

The description gives explicit usage context: use before a selected-face swap, then retrieve results with get_face_detection_results. It does not list cases where detection should be skipped, but it clearly names the relevant workflow and the follow-up tool.

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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