UK used-bike price trends over the last N months by category, from Cyclesite's index series. Example: 'how have road-bike prices changed in 2026?'. Monthly data, refreshed at month-end.
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | 1-24. | |
| category | No |
| 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 cover read-only and idempotent hints. The description adds valuable behavioral context: data is monthly, refreshed at month-end, and sourced from Cyclesite's index. This tells the agent about data freshness and provenance, going beyond the structured annotations.
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 sentences, front-loaded with the core purpose followed by a concrete example. No redundant or filler content. Every sentence contributes to understanding the tool's function and usage.
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 tool's moderate complexity, the presence of an output schema, and annotations, the description covers the essential scope, data source, and refresh cadence. It does not list all categories, but this is not critical given the schema and example. The description is adequate for a read-only query 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?
Schema description coverage is 50% (only months has a description). The tool description clarifies that 'months' means number of months and provides an example category ('road-bike'), but does not enumerate valid categories. It partially compensates for the missing schema description but leaves category values undocumented.
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?
Description clearly states it returns UK used-bike price trends over the last N months by category, with a specific data source (Cyclesite's index series). The example query clarifies the intended use case. This distinguishes it from sibling tools like get_market_health or get_depreciation by focusing specifically on price trends over time.
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 'how have road-bike prices changed in 2026?' implies usage for historical price trend questions, but no explicit guidance on when to use this versus alternatives like get_depreciation or get_valuation. No exclusion criteria or alternative tool references are provided, so usage is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
