arc_lp_apr

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

With no annotations provided, the description carries the full burden. It discloses the key assumption (price stayed in range) and the NO_DATA fallback when no scan exists, which are important behavioral details beyond the schema. It does not mention side effects or auth, but as a read-oriented query tool, these are less critical.

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?

Two concise sentences with no filler. The purpose is front-loaded, filters are briefly listed, and key caveats are stated efficiently.

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?

The description covers the key assumption and the NO_DATA case, but it does not specify the normal return format or structure (e.g., whether it returns a single number, a list, or a table). With no output schema, this omission leaves the agent to guess the response shape, which is a notable gap for a query tool.

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 description coverage is 100%, with each parameter already documented (e.g., 'Filter by pair, case-insensitive, e.g. usdc/weth'). The main description repeats these examples without adding new meaning, so it does not exceed the baseline for fully-covered parameters.

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 the tool computes realised range-APR for Arc LP pools from the latest scan, naming the resource and output. It lists optional filters and distinguishes itself from sibling tools (which cover health, launchpads, token screening, etc.) by its specific subject matter.

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?

Usage is implied—'use this when you need range-APR'—but there is no explicit guidance on when to prefer this over alternatives or when not to use it. No sibling is named or contrasted, and the description only hints at context via 'latest scan' and 'NO_DATA' conditions.

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