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Reassign face

reassign_face
Destructive

Correct face recognition mistakes by reassigning a detected face to the right person; first get face_id from get_asset_faces. This permanently updates the face-to-person mapping.

Instructions

Reassign a detected face to a different person. Use this to correct face recognition mistakes (e.g. a face wrongly attributed to Person A should be Person B). Get face_id from get_asset_faces first. Side effect: permanently changes face-to-person mapping.

Args:
    face_id: The face detection UUID (from get_asset_faces results).
    person_id: The correct person UUID to assign this face to.

Returns: JSON with the updated face assignment.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "face_id": "<face_id>",
      "person_id": "<person_id>"
    },
    "name": "reassign_face"
  }
}

Input Schema

NameRequiredDescriptionDefault
face_idYes
person_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.1

TDQS

A4.2/5.0
Behavior3/5

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

The description does add value with 'permanently changes face-to-person mapping', though this largely restates the destructiveHint=true annotation. It says nothing about what happens to the previously assigned person, whether the change is reversible/undoable, or whether permissions are required, so beyond-annotation disclosure is modest.

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?

Front-loaded purpose sentence, then usage, then side-effect warning, then compact Args/Returns blocks. No filler sentences, and the destructive side effect is called out before the parameter details.

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 and destructive annotations already present, the definition covers intent, prerequisite, parameters, and side effect adequately. A minor gap is that it doesn't state the effect on the previous owner of the face or whether revert_asset_edits can undo it, which matters for a permanent mutation.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the burden and does so: face_id is identified as a face-detection UUID sourced from get_asset_faces, and person_id as the correct person UUID to assign. That gives format and provenance for both parameters beyond the bare typed names.

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?

States a specific verb and resource ('Reassign a detected face to a different person') and immediately frames the intent ('correct face recognition mistakes (e.g. a face wrongly attributed to Person A should be Person B)'). This clearly separates it from related siblings like merge_people, update_person, or get_asset_faces.

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?

Gives concrete when-to-use context (fixing misattributed faces) and a prerequisite workflow step ('Get face_id from get_asset_faces first'). It does not explicitly exclude alternatives such as merge_people when whole identities are duplicated, so it stops short of full routing guidance.

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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