Explain Market Conditions

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

Annotations already indicate a read-only, idempotent, non-destructive operation, so the bar is lowered. The description adds meaningful behavioral context: it states the tool returns structured evidence (observed metrics and associated_changes), explicitly avoids claiming causation, and assigns the explanation task to the calling model. These details go beyond what annotations provide.

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 succinct sentences, front-loaded with the primary action, and every clause earns its keep. It avoids redundancy with schema or annotations while conveying the essential baseline comparison and the no-causation caveat.

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?

The tool has a nested request object and an output schema, so return-value details are not required. The description covers the purpose, baseline default, main output components, and the division of responsibility with the calling model. It is complete for the tool's intended use, though it could mention explicit alternatives for even fuller context.

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 explaining the default baseline window behavior ('immediately preceding equal-length window'), which clarifies the optional baseline_start/baseline_end parameters beyond their schema descriptions. This is meaningful semantic enrichment.

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 a specific verb ('Returns') and resource ('structured evidence for market conditions in a focus window versus a baseline window'), clearly distinguishing it from simple data-retrieval tools. It further differentiates from siblings by stating it does not claim causation and that the calling model produces the natural-language explanation.

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 gives clear context: it is used to obtain evidence for market-condition explanations, with the explicit instruction that the calling model should handle the natural-language explanation. However, it does not name alternative tools like 'compare_market_periods' or state when to prefer one over the other, so it falls short of full alternative guidance.

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