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

No annotations are provided, so the description carries the full burden. It discloses the output structure (signal, regime, percentiles, etc.) and that it provides an interpretation layer, but it does not disclose data freshness, computation method, or any limitations.

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 two sentences, front-loaded with the core resource and features, followed by the target audience. Every sentence adds value; no wasted words.

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 tool with no output schema, the description provides adequate information about what the tool returns. However, it does not explicitly differentiate from fed_rates_v3, which is a sibling; this is a minor gap in completeness.

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 accepts zero parameters, so the schema already fully covers parameter semantics. The description adds no parameter-specific information but none is needed. Baseline 4 for zero parameters.

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 tool as returning Fed Funds Rate data with an interpretation layer (signal, regime, percentiles, momentum, volatility, confidence, actionable_prompt). It distinguishes from sibling fed_rates by explicitly stating the interpretation layer, though it lacks an explicit verb like 'get' or 'retrieve'.

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 phrase 'Built for autonomous agents that need pre-reasoned data' implies when to use this tool; however, it does not explicitly state alternatives or when not to use it, leaving room for ambiguity compared to raw data tools.

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