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media_search

Search Wikimedia Commons by topic to retrieve freely-licensed media files with URLs, dimensions, and license details for reuse.

Instructions

Search Wikimedia Commons for freely-licensed media by keyword — the topic-based counterpart to image (lead image of a known article) and media_list (media already used in an article). Give it a topic ('aurora borealis', 'vintage trains') and get back real Commons files with thumbnail and full-size URLs, dimensions, license, and artist — ready to embed in cards, posts, slide decks, or README hero images. filetype filters to 'image' (default: photos + diagrams / SVGs), 'video', 'audio', or 'all'. Commons is language-independent, so this tool takes no lang parameter. Everything returned is freely licensed; check the license on the file page before reuse.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 10, max 50)
queryYesMedia search keywords (e.g. 'aurora borealis' or 'steam locomotive')
filetypeNoMedia type filter: 'image' (photos + diagrams/SVGs), 'video', 'audio', or 'all'image

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of explaining behavior. It discloses what is returned (URLs, dimensions, license, artist), the default filetype behavior, the language-independent nature, and a licensing caveat. It does not mention error behavior or rate limits, but those are not essential for this kind of search tool.

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 efficiently organized into four sentences: purpose, output detail, parameter semantics, and licensing caveat. It is somewhat dense, but every sentence carries useful information and the main purpose is front-loaded.

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 there is no output schema, the description adequately explains the return values and gives practical context for embedding media. It also covers filtering, language independence, and licensing, leaving no significant gap for an agent to invoke the tool correctly.

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?

Although the input schema already covers all parameters at 100%, the description adds useful semantics: it provides query examples, clarifies that `filetype` 'image' includes photos and diagrams/SVGs, and explains why there is no language parameter. This goes beyond simply restating the 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?

The description opens with a specific verb and resource: 'Search Wikimedia Commons for freely-licensed media by keyword.' It also explicitly differentiates from siblings by naming `image` and `media_list` and describing their distinct use cases, so an agent can immediately understand what this tool does and how it is unique.

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?

It gives clear selection criteria: use this for topic-based searches, `image` for a known article's lead image, and `media_list` for media already used in an article. It also explains why no `lang` parameter exists, preventing an agent from expecting language support.

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