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search_smart

Search your photo library using natural language descriptions of visual content, with optional location and date filters to narrow results.

Instructions

AI-powered visual search using CLIP embeddings. Use this when describing what a photo looks like in natural language (e.g. 'sunset at the beach', 'dog playing fetch'). For structured criteria (city, camera, date), use search_metadata instead. Requires Immich ML service with Smart Search enabled. Read-only.

Args:
    query: Natural language description of the visual content to find.
    city: Optional city filter to narrow results geographically.
    state: Optional state/region filter.
    country: Optional country filter.
    taken_after: ISO date — only assets captured after this date.
    taken_before: ISO date — only assets captured before this date.
    page: Page number, starting from 1 (default 1).
    size: Results per page (1-200, default 50).

Returns: JSON with total count, page, and assets ranked by visual similarity to the query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
cityNo
stateNo
countryNo
taken_afterNo
taken_beforeNo
pageNo
sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, description adds important behavioral details: read-only, returns results ranked by visual similarity, requires specific service. Does not cover potential limits or error conditions, but sufficient for typical use.

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?

Well-structured with front-loaded purpose and usage, followed by a clear Args list. Slightly verbose due to parameter explanations, but no unnecessary sentences.

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 complexity (8 parameters, 10+ siblings), the description covers prerequisites, usage distinctions, return format, and parameter details. Output schema exists, so return format info is supplementary.

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

Parameters5/5

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

Schema coverage is 0%, but description provides thorough explanations for each parameter in the Args section, adding meaning beyond schema fields—e.g., defining query as natural language description and clarifying optional filters.

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?

Description clearly states it's an AI-powered visual search using CLIP embeddings for natural language descriptions, and distinguishes from search_metadata for structured criteria.

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

Usage Guidelines5/5

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

Explicitly tells when to use this tool (describing photo appearance) vs alternative (search_metadata for structured criteria), and mentions the requirement: Immich ML service with Smart Search enabled.

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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