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

With annotations already covering read-only/idempotent safety, the description adds the context that actually matters: refresh cadence (every 30 seconds), the upstream source (https://borro.markets/prices.json), the market-open vs weekend-shutdown pricing difference, and the caveat that quoted execution prices are before Borro's fee. Those are exactly the behavioral traits an agent needs to reason about data freshness and price comparability.

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?

Content is front-loaded — the main payload (what each entry contains) opens the description, followed by two short clarifying sentences on cadence and source. The first sentence is a long run-on with nested parentheses that takes effort to parse, which keeps it from a top score, but no clause is filler.

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?

There is no output schema, so the description carries the burden of explaining return values — and it does, naming each field's meaning including the sign convention for gapPct. Combined with the freshness and fee caveats, an agent has everything needed to call it and interpret the result.

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 tool takes zero parameters, so the baseline of 4 applies; there is nothing for the description to disambiguate. The description does not need to compensate for any schema gap here.

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 names the exact resource (per-stock-token price data) and enumerates the three values returned: the stock's own Chainlink/estimate price, on-chain executable buy/sell prices at $1,000 and $10,000 sizes, and gapPct. That level of specificity lets an agent distinguish this from `markets`, `market`, or `weekend` without opening any schema.

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?

It gives actionable interpretation ('Positive: the token trades over the stock (short it on Borro); negative: under it (buy and lend it)'), which effectively tells the agent what decision this data feeds. It stops short of naming alternatives to use instead (e.g. `weekend` for pure weekend estimates, `markets` for market metadata), so no explicit when-not guidance.

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