Brands ranked by how well (or poorly) they hold their value, from Cyclesite's measured UK used-price data. Returns top N brands by % retained vs new RRP. Example: 'which bike brands hold their value best?'.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | ||
| limit | No | 1-20, default 10. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is established. The description adds context about the data source (Cyclesite UK used-price data) and the ranking metric (% retained vs new RRP), but does not disclose potential edge cases such as data freshness or empty results. This meets the minimal bar given annotations but does not go beyond.
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 sentences plus an example, with no redundant wording. It front-loads the core purpose, then specifies the metric and provides a concrete query. Every sentence contributes value.
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?
Given the presence of an output schema (which explains return values), the description covers the necessary ground: purpose, data source, metric, and example. It does not explicitly mention the sort parameter, but the schema provides the enum, and the description implies both directions. Overall, it is nearly complete for a simple read-only tool.
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?
The description says "how well (or poorly) they hold their value," which maps to the sort parameter's best/worst enum and adds meaning beyond the raw schema. The phrase "top N" aligns with the limit parameter, which already has a schema description. It partially compensates for the 50% schema coverage by clarifying the ranking direction.
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: ranks brands by value retention using UK used-price data, and returns top N by % retained vs new RRP. It distinguishes itself from sibling tools by focusing specifically on depreciation, and the example query reinforces its purpose.
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 example "which bike brands hold their value best?" provides a clear usage context. It implies when to use the tool, but does not explicitly mention alternatives or exclusions. Still, the context is unambiguous enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
