Order Book Stop Map (L4)

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial non-obvious traits: snapshot-derived, refreshed every 60s, latest-only with no history, as_of_height semantics, and an instruction to check age_s before citing. It also flags case-sensitivity ('btc' will 404) and the Pro-tier requirement — well beyond what annotations convey.

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?

Dense but organized, leading with the question it answers and the summary, then the example, then the important window-vs-totals caveat and freshness notes. Nearly every sentence carries load, though the paragraph is long and could be tightened without losing meaning.

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 snapshot tool with no output schema, the description explains both what is returned and how to read it (totals vs buckets, nearest triggers, beyond-window counts) plus freshness (age_s, as_of_height) and addressability constraints. An agent has everything needed to call and interpret 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%, so the baseline is 3, but the description adds genuine meaning: the totals_below/totals_above/nearest_* fields always cover every trigger while `buckets` is limited to the within_pct window, which clarifies how the parameters interact with the output. The case-sensitivity and spelling guidance is largely repeated from the schema, capping it at 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?

Opens with a direct question the tool answers ('where are the stops, and how much size fires if price gets there?') then states the specific operation: bucketing untriggered stop/take-profit orders by distance from mid in 0.25% steps. It explicitly positions itself against the sibling market_orderbook ('the aggregated L2 view; this is the L4 one'), so an agent can distinguish 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?

Provides a concrete usage example ('what is stacked under BTC within 2%?' with the exact call and which fields to read) and clarifies the L4-vs-L2 relationship. It does not enumerate hard when-not conditions or other alternatives, but the context is clear enough to route correctly.

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