predict_sales_cone

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

No annotations are provided, so the description carries the burden. It discloses that the tool uses 'WishlistDoc empirical game discovery models' and that omitted wishlists are estimated, which is useful. However, it does not disclose whether this is a read-only operation, whether it makes external calls, or what the output format looks like. For a forecasting tool, the lack of output schema and return-format details is a moderate gap.

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 with no filler. The core purpose and confidence tiers are front-loaded, and the model provenance is a single clause at the end. Every word earns its place.

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 forecasting tool with no output schema and no annotations, the description should ideally explain what the output looks like (e.g., a table of P10/P50/P90 values) and any assumptions about the input appid. The description covers the model source and optional parameters, but the missing return-format disclosure leaves an agent uncertain about how to interpret the result. Still, the core call is clear.

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%, so the schema already documents all three parameters. The description adds context about the optional wishlist behavior ('If omitted, an empirical estimate...') and the price override, which is helpful. However, it does not add much beyond the schema, so a baseline 3 is appropriate.

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 states a specific verb ('Forecast') and resource ('Steam first-week unit sales and first-year gross revenue') and specifies the three confidence tiers (P10/P50/P90). It clearly distinguishes this from sibling tools like audit_steam_store and get_genre_benchmarks, which are about auditing store data and genre benchmarks rather than forecasting sales.

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 implies when to use this tool: when a sales forecast for a Steam game is needed, and it mentions the optional wishlist input and price override, which gives context for simulation scenarios. It does not explicitly state when not to use it or name alternatives, but the sibling names and the forecast-specific language make the use case clear enough.

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