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

The read-only annotations already cover the safety profile, and the description adds useful scope detail such as the latest column and the 2%/5% bands. However, it does not state freshness, whether the liquidation levels are modeled estimates, the data source, or how the clusters are computed.

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 definition is a single dense sentence with the key scope front-loaded, and the parenthetical earns its place by clarifying that this reflects the latest column. It could be slightly more direct by starting with 'Returns,' but there is no redundant wording.

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 one-parameter read-only tool with full schema coverage, the description explains the content and scope of the result well enough to call it correctly. Because there is no output schema, it could be more explicit about the response fields, but the core return content is covered.

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 the only parameter, market, is fully documented with an enum. The description adds no parameter-specific meaning or formatting guidance, so the baseline score of 3 is appropriate.

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 identifies the resource and analytical output: liquidation levels within 5% of price, USD totals at 2% and 5%, and largest clusters above and below. It is specific enough to distinguish from generic liquidation data, but it does not explicitly contrast with the sibling get_liquidations tool.

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 implies the use case by explaining what the heatmap shows, but it gives no explicit guidance on when to choose this tool over get_liquidations, get_order_book, or get_key_levels. No exclusions or alternative-selection conditions are stated.

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