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

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by characterizing the output as 'illustrative' and a 'scenario planning tool', plus an example conditioned on fed funds and CPI. This goes beyond annotations with important caveats about what the numbers represent, though it does not mention data freshness or potential volatility.

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?

The description is tightly written: three sentences covering use case, return value, caveat, plus one illustrative example and source. Every sentence adds distinct value; no filler or redundant phrases beyond minor overlap between 'illustrative' and 'scenario planning tool'. It is front-loaded with the primary usage trigger.

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, no-output-schema tool with read-only annotations, the description is remarkably complete. It clarifies the purpose, use case, nature of the results (illustrative, not market-implied), provides an example output for shape, and cites the data source. There is no ambiguity about what the tool returns or when to employ 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 tool has zero parameters, so no parameter documentation is needed. The description provides all necessary input context by explaining the data source and example conditioning. This aligns with the baseline expectation for no-parameter tools.

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 action ('Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings') with a clear resource and basis ('current FRED fed funds data'). It distinguishes itself from sibling benchmark tools by focusing on FOMC probability estimates and explicitly notes it is not futures-implied odds, which prevents confusion with market-data tools.

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?

The description opens with an explicit when-to-use statement ('Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session'). It also includes a clear exclusion ('not futures-implied market odds'), but does not name an alternative tool or state 'do not use for...'. This meets the 'clear context, no exclusions' threshold.

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