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

With no annotations provided, the description carries the full behavioral burden. It discloses defaults (day, scope, stat, window_days, top_n), the rationale for scope exclusion, that percentile ranks within visible markets, and that window_days recomputes trailing fields with the response stating the window used. It also warns against guessing field names and points to list_fields. It doesn't explicitly label the operation as read-only, but the query semantics make that obvious; no destructive behavior is implied. This is strong transparency, though not exhaustive on potential errors or rate limits.

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 moderately long but dense with useful information. It front-loads the core purpose in the first sentence, then systematically covers defaults and special cases. Every sentence adds value, and the pointer to screen_universe is a crisp differentiator. It could arguably be tightened, but for a 10-parameter tool the length is justified.

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 of this complexity, the description covers the essential behavioral aspects: what it does, defaults, scope rationale, ranking semantics, window recomputation, volume filtering, and the critical rule about field names. The output schema covers the return structure, so the description needn't repeat that. It also positions the tool against screen_universe. 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?

The schema already documents all 10 parameters with 100% coverage, so the baseline is 3. The description adds meaningful context beyond the schema: it explains how 'day' defaults to the last complete day, why 'scope' matters for comparability, that 'stat' ranks within visible markets, and that 'min_usd_volume_7d_avg' filters dust. It also clarifies that column names must come from list_fields. This enriches the schema's descriptions, 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 opens with a specific verb and resource: 'Rank the whole market on ONE completed day'. It then gives concrete example queries ('top markets by funding / open interest / returns on the latest day' and 'what percentile is BTC on open interest') that unambiguously distinguish it from siblings. It also explicitly names screen_universe as the alternative for percentile cuts, making the tool's niche unmistakable.

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 states exactly when to use this tool (single-day cross-sectional ranking) and when not to ('for a cut... use screen_universe'). It explains scope defaults and why hip3 markets are excluded ('their flow is not comparable'), and notes that percentile/zscore rank within visible markets. This is explicit guidance with a named alternative, leaving nothing to inference.

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