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Get images batch

get_images_batch
Read-only

Fetch thumbnails for asset IDs and return image blocks for inline visual display of search results.

Instructions

Get thumbnails for arbitrary asset IDs as image blocks for inline visual display. Use this to visually show search results in clients that render images. For HTML gallery generation with base64 data URIs (Cowork/skills), use get_thumbnails_batch instead — it returns JSON with filenames and dates. Read-only.

Args:
    asset_ids: List of asset UUIDs to fetch thumbnails for.
    size: 'thumbnail' (250px) or 'preview' (1440px). Default: 'thumbnail'.
    limit: Max thumbnails to return (1-50, default 20). Only the first N IDs are fetched.

Returns: A list of image blocks suitable for visual display.

Input Schema

NameRequiredDescriptionDefault
sizeNothumbnail
limitNo
asset_idsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.1

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's 'Read-only' is consistent with that. Beyond the annotations it adds genuinely useful behavior: the output is image blocks rather than JSON, and 'Only the first N IDs are fetched' discloses truncation semantics that would otherwise surprise a caller. It stops short of covering error handling or per-request limits.

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 with the core action and the sibling routing, then organized into Args and Returns sections. No filler; every sentence either routes the agent, defines a parameter, or states the return shape.

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?

For a 3-parameter read tool with no output schema, the description supplies the selection rule, the alternative, all parameter semantics including defaults and ranges, and the return type. An agent has everything needed to call it correctly and know what comes back.

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 description coverage is 0%, so the description carries the full burden and does so: asset_ids as UUIDs, size with concrete enum values and pixel dimensions ('thumbnail' 250px, 'preview' 1440px) not present in the schema, and limit with its 1-50 range, default 20, and truncation behavior. This adds substantive meaning well beyond the bare property 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 (get) and resource (thumbnails for asset IDs) and describes the exact output form (image blocks for inline visual display). It also names the sibling get_thumbnails_batch and the condition that separates them, so an agent can distinguish it without opening any schema.

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 says when to use it (visually showing search results in clients that render images) and when not to (HTML gallery generation with base64 data URIs), naming the alternative tool and what that alternative returns. This is exactly the when/when-not/alternative guidance that makes selection reliable.

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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