APIbase

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond these: the data scale (816K+ series), the authoritative source (Federal Reserve), and concrete examples of retrievable indicators. It does not contradict annotations and supplements them with domain-specific context.

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?

Single sentence with zero waste. Front-loads the action ('Get US economic data'), follows with scale ('816K+ FRED series'), and closes with authoritative source and examples. Every clause earns its place with high information density.

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?

Given 100% schema coverage, good annotations (readOnly, idempotent), and no output schema, the description provides sufficient context for tool selection by clarifying the data source and available indicator types. Minor gap: does not hint at return format (time series observations), though this is partially mitigated by the 'observation' parameter names in the schema.

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 100% schema description coverage, the baseline is 3. The description adds value by mapping human-readable indicator names (GDP, CPI, unemployment) to the series_id parameter, complementing the schema's technical ID examples (e.g., 'CPIAUCSL'). This semantic bridging helps agents understand what valid series IDs represent conceptually.

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 uses specific verb 'Get' with clear resource 'US economic data' and scope '816K+ FRED series'. It explicitly distinguishes from siblings like 'finance.macro.country' by specifying the US focus and Federal Reserve source, while listing concrete examples (GDP, CPI, unemployment) that clarify the tool's domain.

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 boundaries by restricting scope to 'US economic data' and FRED series, which helps distinguish from sibling tools. However, it lacks explicit 'when to use' guidance or named alternatives (e.g., it doesn't state to use 'finance.macro.country' for non-US data), leaving some inference required by the agent.

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