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

With no annotations, the description carries the full burden and does so well: it discloses the data source (public trade stream), the resulting coverage limitation (covered_long_pct/covered_short_pct grow over time), the methodology ('Not a model'), and the return contents. This is exactly the provenance/caveat context an agent needs to trust and interpret the output.

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?

Information-dense and front-loaded with the venue tag and safety flag; nearly every clause earns its place. The bracketed tag prefix and stacked parentheticals make it slightly telegraphic, but nothing is padding.

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?

No output schema, so the description supplies the return shape (long/short notional per bin, cumulative curves, largest positions). Combined with the sibling routing, coverage caveat, and range values, an agent has everything needed to call and interpret it.

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 0%, so the description must compensate. It fully enumerates the range enum ('12h | 1d | 3d | 7d | max'), which the schema lacks entirely, and the required symbol is inferable from context. Symbol format/venue conventions remain unstated, keeping it just short of a 5.

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 ('REAL open positions on Lighter ... binned by Lighter's own liquidation price') and immediately distinguishes itself from lookalike siblings by naming get_hl_liquidation_map and get_liquidation_map. The 'Not a model' clause nails the key distinction between measured vs. modeled data.

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?

Explicit routing: 'Use this for Lighter; get_hl_liquidation_map for Hyperliquid; get_liquidation_map (modeled) for the CEXs.' This tells the agent not only when to use it but which alternative to pick for each other venue.

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