macro_bank_usa_interest_rate

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds valuable context beyond annotations: the data range, a source URL, and the return type (pandas.Series with values in %). This provides operational expectations without contradicting the annotations.

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 extremely concise, containing only essential information: report name, data range, source URL, and return type. No filler or redundant text; every line earns its place.

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-only tool with annotations, the description provides enough context: data range, source, and return type. It does not include an output schema, but the return type is stated. It lacks nuanced details like frequency or timezone, but the tool is simple enough that this is acceptable.

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 input schema is empty (0 parameters), so the baseline is 4. The description adds no parameter details but clarifies the return semantics (current value in %) and data range, which is sufficient given no parameters exist.

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 clearly identifies the resource as the US Federal Reserve interest rate decision report, specifies the data range (19820927 to present), and states the return format (pandas.Series of current values in %). This distinguishes it from sibling tools for other countries' interest rates.

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 implies usage by stating it covers the Fed's interest rate decisions, but it does not explicitly state when to use this over alternatives or mention exclusions. There is no direct comparison to sibling tools like macro_bank_euro_interest_rate, so guidance is only implicit via the name and description.

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