Yield curves

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description's added value is in describing what comes back: YTM in percent for horizons in days, at three time snapshots (now, one month, six months). That return-shape context is genuinely useful given there is no output schema, though it doesn't discuss gaps, interpolation limits, or empty-curve behavior.

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 sentences with no filler. The resource and scope come first, and the operational guidance (how to use it alongside ifb_yields) follows — properly front-loaded and scannable.

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 zero-parameter read with no output schema, the description supplies the essential context: what a curve contains, units (percent, days), and the three time snapshots. Minor gaps remain around response structure (e.g., how horizons are enumerated) but nothing that would block correct invocation.

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 is 4; there is nothing for the description to disambiguate, and it correctly implies a parameterless snapshot call by describing fixed instrument classes and time snapshots rather than configurable inputs.

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 ('Get Fara Bourse's fitted yield curves') and narrows scope by naming the model (Svensson/Nelson) and the three instrument classes covered. It also implicitly distinguishes itself from the sibling ifb_yields, which returns individual paper YTMs rather than a fitted curve.

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?

Gives a concrete use case — pricing a bond by comparing an `ifb_yields` YTM against the curve at the paper's remaining days — which tells the agent both when to call it and how it pairs with a sibling. It stops short of stating when *not* to use it (e.g., for raw historical yields, use ifb_history), so it's clear context without explicit exclusions.

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