discovery_ratio

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

With no annotations, the description carries the full behavioral disclosure burden and adds real value by stating the quota shape: 'GET /me/player/recently-played + 1× GET /me/top/tracks,' which signals cost and implies read-only behavior. It does not explicitly state no side effects, auth requirements, or edge cases like an empty recently-played history — adequate but not exhaustive.

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 zero filler: the metric definition is front-loaded, and the quota sentence earns its place as cost-critical context an agent needs before invoking the tool.

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 low-complexity compute tool with three optional, fully documented parameters, this covers the essentials: metric definition, default comparison window, and call cost. The missing output schema is mitigated by response_format's schema description of the three output modes; the remaining gap is edge-case behavior, which is a minor omission.

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 coverage is 100% and every parameter already has a meaningful schema description (defaults, enum semantics, API paging behavior). The description adds only the conceptual frame 'discovery vs staple listening' and echoes the medium_term default, so the baseline 3 applies without further compensation needed.

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?

States a specific verb and resource: 'Measure what share of your recently-played tracks are NOT in your top tracks.' This defines the computed ratio and differentiates it from raw-data siblings like get_ecently_played and get_top_tracks without needing to name them.

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 metric definition plus 'default compares against medium_term' gives clear context for when the tool is relevant, and the quota line signals cost before calling. However, it names no alternative tool or exclusion (e.g., when discovery_digest would be the better choice), so usage guidance remains implied rather than explicit.

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