Current UK used-bike market prices by category, from Cyclesite's nightly index. Returns median + range per category (road, mtb, gravel, e-bike, etc.). Example: 'how does the UK used-bike market look right now?'. Refreshed nightly from real UK market prices.
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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?
Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses the data source ('Cyclesite's nightly index'), refresh cadence ('refreshed nightly'), and return shape ('median + range per category'). This adds meaningful behavioral context without contradicting 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?
The description is three sentences, front-loaded with the core purpose, and includes a practical example query. Every sentence adds value with no redundant or filler content.
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 parameterless read-only tool with an output schema, the description is complete. It explains what data is returned, from where, how often it updates, and provides an example. No significant gaps remain.
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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters but adds semantic context about the output categories and data freshness, which helps set expectations.
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 reports current UK used-bike market prices by category, with specific example categories (road, mtb, gravel, e-bike). It uses a specific verb ('get') and resource ('market index') and is distinguishable from sibling tools like get_market_health or get_price_trends by focusing on current snapshot prices.
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 a clear use case with an example query ('how does the UK used-bike market look right now?'), implying when to use it. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it for current market price queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
