Wikimedia-Search-Images
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching for images on Wikimedia Commons with metadata and optional thumbnails.
Naming Consistency5/5The single tool name 'wikimedia_search_images' follows a consistent verb_noun pattern, using snake_case and clearly indicating the action (search) and target (images). There are no other tools to compare against, so consistency is inherently perfect.
Tool Count2/5A single tool is too few for a server named 'Wikimedia-Search-Images', which suggests a broader scope for image-related operations. While the tool covers search well, the lack of additional tools (e.g., for fetching, filtering, or managing images) makes the set feel incomplete and limited in functionality.
Completeness2/5The tool set is severely incomplete for the implied domain of Wikimedia image operations. It only provides search functionality, with no tools for actions like downloading full images, viewing metadata details, or handling image collections, leaving significant gaps that could hinder agent workflows.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that results include 'metadata including download URLs and optional thumbnail composite image,' which gives some insight into outputs. However, it fails to disclose critical behavioral traits such as rate limits, authentication requirements, pagination behavior (beyond offset/limit parameters), error conditions, or whether this is a read-only operation. For a search tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured in two sentences. The first sentence front-loads the core functionality, and the second provides a usage example. There is no wasted verbiage, and each sentence adds value, though it could be slightly more detailed without losing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks parameter explanations, detailed behavioral context (e.g., rate limits, errors), and output specifics. While it covers the basic purpose and a usage hint, it doesn't provide enough information for an agent to confidently invoke the tool without guessing about parameters or behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 5 parameters have descriptions in the schema. The tool description does not explain any parameters—it doesn't mention 'query,' 'limit,' 'offset,' 'license,' or 'include_thumbnails,' nor does it clarify their purposes, formats, or constraints. With low coverage and no compensation in the description, this falls short of the baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for images on Wikimedia Commons with metadata including download URLs and optional thumbnail composite image for visual comparison.' It specifies the verb ('Search'), resource ('images on Wikimedia Commons'), and key outputs (metadata, download URLs, thumbnails). However, without sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance: 'Use results to e.g. fetch full images that are relevant for your task.' This suggests a workflow where this tool is used for discovery before fetching images. However, it lacks explicit when-to-use rules, prerequisites, or comparisons to alternatives (none exist here), making it adequate but not comprehensive.
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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