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

Annotations already establish read-only, idempotent, non-open-world behavior. The description adds genuinely useful context beyond them: the data is model output recomputed nightly from public filings and end-of-day prices, which tells the agent about freshness and latency expectations, plus explicit exclusions (not real-time quotes, not investment advice).

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 dense sentences, front-loaded with the actual contents before provenance and disclaimers. The parenthetical enumeration is long but each element earns its place by telling the agent what is returned.

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?

With no parameters and no output schema, the description carries the return-value burden and does so by enumerating the included series, provenance, and caveats. It lacks detail on units or response shape, which keeps it just short of fully complete.

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 and there is nothing for the description to disambiguate. No parameter-level meaning is needed or missing.

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 a concrete resource (US Treasury yield curve, risk-free rate, equity risk premium, SweetValueLab seven-factor sentiment gauge) and its scope, so an agent can tell it apart from get_stock_valuation and search_stocks by content. It stops short of explicitly naming those siblings, so it is clear but not maximally differentiated.

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 rather than stated: mentioning 'the risk-free rate and equity risk premium the valuation models discount with' hints this is a companion input to valuation work. There is no explicit when-to-use vs when-not guidance or named alternative, only negative boundaries (not real-time, not advice).

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