get_stress_summary

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

No annotations are provided, so the description must carry behavioral disclosure. It clearly notes the compact output size (~400 bytes vs ~35KB), making the response behavior predictable, but it omits potential side effects, errors, or read-only semantics.

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?

Description is brief and to the point, with no redundant information. The byte-size comparison and ideal-use note add value without bloating the text.

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 simple one-parameter tool, the description provides the necessary context: what it returns (compact summary), why it exists (lightweight), and the input format. It does not enumerate the specific metrics in the summary, but the core use is clear.

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 schema only defines 'date' as a required string with no property description, so the docstring's explicit 'YYYY-MM-DD' format is essential and compensates for the schema gap. Additional semantics like allowed date ranges or timezone are not covered.

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?

Clearly identifies the resource (stress summary), the action (get), and distinguishes itself as a lightweight version returning compact data instead of full time-series. This differentiates it from sibling tools like get_stress_data.

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?

States ideal use cases (daily health checkups, LLM integrations) and explicitly contrasts with full time-series data, guiding when to prefer this tool. It does not name sibling alternatives explicitly, but the usage context is clear.

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