consumer-brief

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

With no annotations provided, the description must disclose behavioral traits. It does mention that the tool uses GPT-4o-mini and fetches 8 FRED signals, implying a read-only, external-data-dependent process. However, it lacks details on data freshness, latency, potential costs, or whether the LLM call introduces variability. It does not contradict any 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 a single paragraph that efficiently packs key information: data sources, LLM usage, output components, and use cases. It is front-loaded but could be slightly better structured with bullet points or clearer separation of input/output.

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 no output schema and zero parameters, the description thoroughly explains the tool's behavior. It lists the 8 FRED signals, the LLM synthesis step, the output components (including narrative and risk), and the intended applications. This provides a complete understanding of what the tool returns and when to use 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 input schema is empty (0 parameters), so schema coverage is trivially 100%. The description compensates by explaining what the tool does without needing parameters, clearly implying no user input is required. While no parameter-specific info is needed, the description adds value by listing the underlying data sources and outputs.

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 states the tool's purpose: it produces an AI-synthesized US consumer health briefing by fetching 8 specific FRED signals and using GPT-4o-mini for synthesis. The verb 'fetches' and 'produces' specify action and output, and the detailed list of output components distinguishes it from sibling tools like macro-brief or equity-brief.

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 implicitly provides usage context by listing the tool's inputs (8 FRED signals) and outputs (consumer posture, spending regime, etc.), indicating it is appropriate for US consumer economic analysis. It mentions specific use cases like retail sector exposure and consumer credit risk, but does not explicitly contrast with alternatives or provide when-not-to-use guidance.

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