Skip to main content
Glama

Regenerate people of users in cluster group

immich_cluster_group_regenerate_people

Re-run facial recognition for all faces of users in a cluster group to regenerate people assignments after changes or errors.

Instructions

Regenerate people of users in cluster group

Forcefully re-run facial recognition for all faces of users in this group.

Immich operation: POST /cluster-groups/{id}/regenerate-people · tag: Cluster groups

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesformat: uuid

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the safety profile is covered. The description adds that the re-run is 'forceful' and applies to 'all faces of users in this group', which gives scope and impact context, but it omits side effects, permissions, or whether existing people data is overwritten. No contradiction with annotations.

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?

The description is short and front-loaded, but the first line repeats the title verbatim, which is redundant. The remaining two lines (facial-recognition scope and API operation) are efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations covering the safety profile and the schema fully documenting the single parameter, the description is adequate for a simple mutation. However, it lacks any indication of what the operation returns or whether it is asynchronous, which an agent might need given no output schema exists.

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% and the single id parameter is documented as uuid format. The description adds no parameter-level detail, so baseline 3 applies when schema does the heavy lifting.

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 (regenerate) and resource (people of users in cluster group), and clarifies the operation as re-running facial recognition for all faces of those users. It does not explicitly differentiate from sibling facial-recognition tools like immich_reassign_faces or immich_get_faces, so a 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance. The description implies the operation but does not name alternatives or conditions that select this tool over siblings such as immich_get_cluster_group_users or immich_reassign_faces.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools