Match a cat description to likely breeds from furriq.com's curated database of 61 breeds. Describe what the cat looks like or how it behaves (coat pattern, body shape, ears, temperament) and get the top candidates with the specific markers that matched. Deterministic — no AI guessing.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Description of the cat, e.g. "large fluffy cat with a bushy tail and lynx-like ear tufts" or "small hairless wrinkly cat". |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden and does disclose real traits: the matching is deterministic ('no AI guessing'), it draws from a fixed 61-breed curated database, and it returns top candidates with matched markers. It omits what happens on a poor/no match and any input limits.
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?
Three tight sentences, front-loaded with the action and followed by input expectations and output nature. No filler or repetition.
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 single-parameter read tool with no output schema and no annotations, the description covers input style, data source, and result shape well. It could add no-match behavior or result limits, but nothing essential to invocation is missing.
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 the baseline is 3, but the description adds genuine query-construction guidance by enumerating which attributes to include (coat pattern, body shape, ears, temperament) and promising marker-level output, beyond the schema's two examples.
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 and resource ('Match a cat description to likely breeds') plus scope ('curated database of 61 breeds'). It implicitly separates itself from analyze_cat_photo by requiring a text description rather than a photo, but it never names a sibling explicitly.
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?
It tells the agent how to supply input ('Describe what the cat looks like or how it behaves') but gives no when-to-use/when-not guidance and never contrasts itself with analyze_cat_photo or list_breeds. Usage is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.