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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses key operational traits: coverage start date (2025-09-15), default baseline exclusion, grouping semantics (game vs. dimension), and corrects a likely misconception (not a release date). This gives agents a solid understanding of what the tool returns and how it handles data edges.

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 entire description is three succinct sentences, front-loaded with the core purpose, followed by essential clarifications. Every sentence adds necessary information; there is no fluff or repetition. The structure leads with purpose, then caveats, then grouping options—optimal for quick agent consumption.

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 an 11-parameter tool with no output schema and no annotations, the description covers essential operational context: coverage date, baseline behavior, and grouping semantics. It also clarifies the tool's scope (not release dates). While it doesn't describe the result structure, given the sibling tools and the 'find games' phrasing, the expected return is reasonably clear. Minor gaps like country selection rules are deferred to the parameter descriptions, which is acceptable.

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 baseline is 3. The description adds meaningful semantic value for two pivotal parameters: group_by (explains deduplication vs. original dimensions) and include_baseline (why it defaults false and the implication of including baseline day). This goes beyond the schema's basic descriptions, compensating for the potential ambiguity of these parameters.

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 verb 'find' and the specific resource 'games first observed in official daily app-store charts' with dimensions (package, platform, country, category, rank type). It explicitly distinguishes from a release date and names the unique grouping behaviors, setting it apart from sibling tools like get_store_game_rank which focus on ranks rather than first appearance.

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 provides clear context on when to use this tool (detecting first chart appearances) and explains critical usage details such as coverage start date, baseline exclusion, and grouping modes. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full exclusion or alternative guidance.

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