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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the description correctly focuses on data semantics. It adds useful behavioral context — 14 tenors, linear interpolation, and the no-extrapolation constraint — though it leaves the length and granularity of the 'recent history' unspecified.

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 tight sentences with zero waste. The core resource and its defining constraints are front-loaded, and the closing sentence adds relevant context about how the curve is consumed.

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 tool with rich annotations and no output schema, the description covers what the curve is and its key mathematical constraints. The main remaining gap is that the scope of 'recent history' is not specified, which an agent might need to know before using the data.

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?

With zero input parameters there is nothing for the schema to document, so parameter semantics are not a meaningful differentiator. The description appropriately avoids inventing parameters and confirms the tool is a no-argument data fetch.

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 states a specific resource — the US Treasury par-yield curve used on the pricing date — with concrete attributes (14 tenors, linear interpolation, no extrapolation, plus recent history). It is clearly distinct from sibling tools like ncsx_bond or ncsx_constituents, though it does not explicitly name an alternative.

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 only implied: the pricing-date context and the note that every constituent spread is measured against this curve suggest when an agent would need it, but there is no explicit when-to-use or when-not-to-use guidance. No alternatives among the sibling tools are mentioned.

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