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

Annotations already declare readOnly, openWorld, and idempotent hints. The description adds valuable behavioral context by disclosing the data sources (Cyclesite's live UK inventory plus catalogue record), the aggregation logic ('most-common'), and the conditional inclusion of motor and battery specs for e-bikes. This goes beyond what annotations provide, warranting a score above baseline.

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?

Two polished sentences: the first defines the tool, the second lists the returns and gives a concrete example. Every phrase adds value, and the information is front-loaded with the tool's purpose. No redundancy or filler.

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 input structure (brand+model+year), the output content (listed specs), data sources, and a use case. It does not discuss what happens when no data is available or how the aggregated results are computed in detail, but an output schema exists and the description is sufficient for the tool's straightforward read-only nature.

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 0%, so the description carries the burden. It does mention 'brand+model[+year]' and uses brackets to indicate optionality of year, plus an example with brand and model. However, it does not elaborate on valid values, formats, or constraints beyond what the schema names/type already conveys, leaving some ambiguity about how to format values (e.g., casing, full names).

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 clearly states the tool's function: providing an aggregated spec sheet for a brand+model+year combination, and it enumerates the exact attributes returned (frame material, wheel size, groupset, etc.). It distinguishes itself from siblings by emphasizing aggregation from live inventory and catalogue data with an example usage ('what groupset does a Canyon Endurace usually have?').

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 implies a clear use case: retrieving typical or most-common specifications for a bike model, as shown by the example. However, it does not explicitly mention when not to use this tool or suggest alternative sibling tools, so it misses the 'explicit exclusions/alternatives' criterion for a 5.

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