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get_asset_faces

Retrieves all detected faces in a photo with their person assignments. Use to see who is in a photo or get face IDs for reassignment.

Instructions

Get all detected faces in a photo with their person assignments. Use this to see who is in a specific photo or to find face IDs for reassign_face. Read-only.

Args:
    asset_id: The asset's UUID.

Returns: JSON array of face detections (each with face_id, person_id, person_name, bounding box).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asset_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler that calls ImmichClient.get_asset_faces(asset_id) and returns JSON-serialized face detections.
    @mcp.tool()
    async def get_asset_faces(ctx: Context, asset_id: str) -> str:
        """Get all detected faces in a photo with their person assignments. Use this to see
        who is in a specific photo or to find face IDs for reassign_face. Read-only.
    
        Args:
            asset_id: The asset's UUID.
    
        Returns: JSON array of face detections (each with face_id, person_id, person_name, bounding box).
        """
        result = await _client(ctx).get_asset_faces(asset_id)
        return json.dumps(result, default=str)
  • Helper method that sends GET /faces with asset_id param to the Immich REST API and returns the list of detected faces.
    async def get_asset_faces(self, asset_id: str) -> list[dict]:
        """Get all detected faces for an asset."""
        return await self._request("GET", "/faces", params={"id": asset_id})
Behavior3/5

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

Declares 'Read-only' and describes return shape. No annotations exist, so description carries burden. Lacks details on permissions or error conditions.

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?

Short, front-loaded purpose, uses clear sections for Args and Returns. Every sentence adds value.

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?

Describes return format despite no output schema. Lacks error handling or permission details but adequate for a simple read operation.

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?

The schema provides no description for asset_id. The description adds 'The asset's UUID,' clarifying the parameter. This adds value beyond the raw schema.

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?

Clearly states it retrieves detected faces with person assignments. Differentiates from siblings like reassign_face (modifies) and get_person (single person).

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?

Explicitly guides to use for seeing who is in a photo or finding face IDs for reassign_face. Lacks 'when not to use' but positive context is strong.

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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