Buyer's-vs-seller's market signal for the UK used-bike market — should the user buy or sell now? Composite indicator from days-to-sell, asking-vs-sold-price spread, and inventory levels. Example: 'is now a good time to buy a road bike?'. Refreshed nightly.
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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?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds valuable context beyond annotations: it explains the tool is a composite indicator of days-to-sell, asking-vs-sold price spread, and inventory levels, and notes that it is refreshed nightly. This enriches the agent's understanding of what the signal represents and its freshness.
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 tight sentences: purpose, composition, and example+refresh cadence. Every sentence adds distinct value, with no redundancy or filler. The structure front-loads the key question (buy or sell) and then elaborates efficiently.
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 has no parameters and an output schema exists, the description is complete for selection and invocation: it specifies the market (UK used-bike), the composite nature, the example use case, and the refresh schedule. It covers both what the signal is and how to think about it, with no outstanding gaps.
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 description has no parameter details to explain—it fully captures the tool's meaning and behavior. The baseline for zero-parameter tools is 4, and the description earns that by clearly explaining the tool's output significance without needing param-centric detail.
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 purpose: providing a buyer's-vs-seller's market signal for the UK used-bike market, with a specific composite indicator. It includes an example question ('is now a good time to buy a road bike?') that clarifies intended use, and the phrasing distinguishes it from sibling tools like get_market_index or get_price_trends by focusing on the buy/sell recommendation angle.
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 clear context for when to use the tool: when assessing whether to buy or sell now, with a concrete example. It does not explicitly name alternative tools or exclusion criteria, but the context is unambiguous enough for an agent to select it appropriately among peers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
