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

The annotations already cover the safety profile (readOnlyHint true, destructiveHint false, openWorldHint true), so the description's job is to add context beyond that. It does: it states that there is no input ("No input"), that every figure carries its own date (freshness semantics), that "P = preliminary" flags provisional values, and it discloses the EFFR Terms of Use plus the NY Fed non-affiliation. It stops short of describing the shape or units of the returned payload, which matters because there is no output schema.

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?

It leads with the useful framing (what the figures are, sourced from primary public data, each with its date), which is well front-loaded. But it then strings everything into one long run-on sentence, and the closing "Toll is not affiliated with the New York Fed" legal disclaimer adds little for tool selection.

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 output schema, the description has to carry the return-value burden, and it does: it enumerates each series returned (yield curve maturities 1m-30y, EFFR plus target range, CPI-U YoY, unemployment rate, NFP monthly change) and notes the date on each. The remaining gap is the payload's shape and units (percent vs thousands of jobs), which an agent would still have to discover by calling it.

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 schema has zero parameters and the description explicitly states "No input." That confirms the tool takes no arguments, so the baseline of 4 applies with no ambiguity for the agent.

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 specific datasets it returns: the Treasury daily par yield curve, the NY Fed EFFR with its target range, and BLS CPI-U, unemployment, and nonfarm payrolls. This is a concrete statement of the resource. However, it does not explicitly contrast the tool with siblings such as fundamentals or stock_token, so the agent must infer that this is the market-wide macro series rather than per-token data.

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?

Naming the audience ("agents reading Robinhood Stock Tokens on Robinhood Chain") and calling the sources "public primary sources" implies when the tool fits: macro context attached to token research. But there is no explicit rule for when to call it versus siblings such as fundamentals, and no stated exclusion or prerequisite. Usage is therefore implied rather than directed.

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