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

No annotations are present, so the description carries the full burden. It discloses key behavioral details: the daily series is 'sampled,' sales volume and Robux are for 'the latest measured day,' the market cap formula is given, and 'Item-for-item trades are excluded.' This is a solid disclosure of what the tool does and excludes, though it does not cover auth, rate limits, or output format.

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?

Three sentences deliver a complete picture: what is returned, how the core metric is computed, and when to use it. There is no filler or repetition; every clause earns its place, and the most important scope ('over any date range, split between Classic and UGC') is front-loaded.

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 single-parameter tool with no output schema, the description covers the returned fields, the sampling behavior, the formula, and an explicit exclusion. The only minor gap is not describing the exact structure or granularity of the returned series, but the listed metrics and 'sampled daily series' give an agent enough to reason about the output.

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 100% for the single 'days' parameter, with a detailed enum explanation including 9999 semantics. The description adds no additional meaning beyond the schema's own documentation, so the baseline score of 3 applies.

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 opens with a specific verb and resource: 'How big the Roblox Limited market is over any date range, split between Classic and UGC Limiteds.' It enumerates the exact metrics returned (market cap, items, copies, sales volume, Robux) and defines market cap as 'RAP of each item times the copies of it that exist.' This clearly distinguishes it from broad siblings like get_market_stats by focusing on the Classic vs UGC split and time-series aspect.

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?

Provides explicit use cases: 'Use it for how big the market is, whether it is growing, how marketplace sales compare, and how the two halves differ.' This tells an agent when to call this tool, though it does not explicitly mention alternatives or when not to use it, leaving some inference to the agent.

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