Free canonical search for Japanese anime figures and collectibles. Use this before paid tools when the exact product is uncertain.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ||
| query | Yes | Name, JAN/EAN-13, model number, character, franchise or series. |
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| results | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It only says the search is 'free' and 'canonical' but does not explain what constitutes canonical, how search results are returned, whether partial matches are supported, or any external data source behavior. This is a significant gap for a 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The core purpose is front-loaded, and the usage guidance is concise and actionable. Every word 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?
The description covers the main search purpose, the free/paid positioning, and the condition for use. However, it omits any detail about the lang parameter, does not name the paid sibling tools, and provides no behavioral context about search matching or result characteristics. The output schema helps, but the description alone leaves gaps.
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 description coverage is only 50%; the query parameter is documented well in the schema, but the lang parameter has no description. The tool description does not explain lang or add parameter-level meaning. It adds context about the search scope but does not compensate for the undocumented lang parameter.
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?
The description states a specific action ('free canonical search') and resource ('Japanese anime figures and collectibles'), which clearly defines what the tool does. It distinguishes itself from paid tools by noting it is free and should be used first, though it does not name a specific sibling tool like identify_anime_product as the direct alternative.
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?
The description gives explicit usage guidance: 'Use this before paid tools when the exact product is uncertain.' This clearly indicates when to use the tool and implies the alternative is paid tools. However, it does not explicitly state when not to use it or name the specific sibling tools that are the paid alternatives.
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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Most tools target a distinct decision or data need: identity, market price, rarity, authenticity, buy/wait decision, and best purchase route. The main ambiguities are search_anime_product vs identify_anime_product and anime_market vs best_place, but the descriptions largely clarify the intended use.
The naming is readable but mixes conventions: four tools use an anime_ noun prefix, two use verb_anime_product, and two use standalone noun phrases like best_place and full_intelligence. The pattern is not predictable enough for a consistent verb_noun or prefix-based convention.
Eight tools is a well-scoped count for an anime-collectible intelligence service. Each tool maps to a necessary step in the decision workflow, and full_intelligence is a justified aggregation endpoint rather than redundant bloat.
The workflow from search/identify through market, rarity, authenticity, buy/wait, and best place covers the core advisory journey well. Minor gaps like price history or an explicit watch-list mechanism are absent, but agents can work around them using market data and the watch recommendation.