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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It thoroughly covers what the tool returns (series ascending, latest, window_mean, n_days), the exclusion of the forming day, the window mechanics via `days` with tier caps, and the fact that it returns a derived ratio rather than raw volumes. This is comprehensive transparency for a read-only data retrieval tool.

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 dense but well-structured, front-loading the core question and definition, then the alternative, then return details. The rhetorical opening adds personality but is not strictly necessary. The text is slightly longer than needed, but every sentence contributes meaningful information, so it earns a 4.

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 tool with two optional parameters and no output schema, the description covers all essential aspects: what the metric means, how to interpret it, what is returned, edge cases (forming day excluded), and tier constraints. It also addresses the primary use case and directs to the correct sibling. Nothing critical is missing for an agent to use it correctly.

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% for both parameters (days and pair), so baseline is 3. However, the description adds valuable context beyond the schema: it explains the meaning of the `days` window in terms of UTC day aggregation, the tier-based capping (Free/Pro/Power), and that the `range` block declares requested/granted values. This enriches parameter understanding, justifying a 4.

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 what the tool does: it computes daily SPOT taker-buy share for a Binance pair, indicating whether taker buys or sells dominate. It uses a specific verb (get) and resource (taker imbalance) and explicitly distinguishes itself from futures positioning by naming the sibling arena_get_funding_rate_history. The metric definition (0..1 scale, 0.5 balanced) is precise, making the purpose unambiguous.

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 explicitly states when to use it (to check spot aggressor flow) and when not to (for leverage positioning), directing the user to the alternative tool. It also clarifies that it returns daily series, implying suitability for trend analysis. This provides clear usage context and exclusion criteria, going beyond what the schema offers.

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