Searches the media library (all images AI-generated) by keywords in the title, the tags, the file name or the album and returns ready URLs, 100 per page.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page of the result, 100 images per page (optional, default 1). | |
| query | Yes | Search term, e.g. 'gradient', 'laptop', 'wald'. |
Changes observed during successful MCP inspections.
Input schema / properties / pageAdded value: +{
+ "description": "Page of the result, 100 images per page (optional, default 1).",
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / query / descriptionPrevious value: -"Suchbegriff, z. B. 'gradient', 'laptop', 'wald'."New value: +"Search term, e.g. 'gradient', 'laptop', 'wald'."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose the search scope (title, tags, file name, album), the return type ('ready URLs'), and pagination ('100 per page'), which is useful context, but it says nothing about result ordering, empty-result behavior, or any auth/rate constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence that covers purpose, search fields, return format, and pagination without any wasted words – the verb leads and every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter read tool with no output schema and no annotations, the description supplies search semantics, return format, and page size. It is nearly complete, only lacking detail on result ordering or page-count/termination behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both parameters are already documented (baseline 3). The description goes one step beyond by explaining that the query term is matched against title, tags, file name, and album, adding semantic meaning not present in the schema's example values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Searches') and resource ('the media library') and clarifies the corpus ('all images AI-generated'), which cleanly separates it from list_images. It stops short of explicitly naming the sibling alternatives, so it's clear but not fully differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied – reach for this when you need keyword search rather than enumeration – but there is no explicit 'use this when / use list_images otherwise' guidance and no exclusions or prerequisites stated.
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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