oura_get_heart_rate

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

Discloses key behavioral traits: high-volume data, source tagging (awake, rest, etc.), and output format differences. These go beyond the annotations' read-only hint, giving full context for selection and invocation.

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?

Well-structured with a clear topic sentence, usage advice, and format breakdown. Slightly verbose but every sentence adds value; could be tightened slightly but no waste exists.

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?

Fully covers the tool's complexity given 4 parameters with 100% schema coverage, annotations present, and an output schema. No gaps in context for correct agent usage.

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?

Adds significant meaning beyond the 100% schema coverage: explains the purpose of response_format values ('summary' vs 'raw') and how limit caps raw samples. The description clarifies usage context such as 'capped at limit; narrow the window for more.'

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 'fine-grained heart-rate timeseries' and explains sampling frequency, distinguishing it from sibling tools like oura_get_sleep_detail or oura_get_workouts. It provides specific verb-resource mapping.

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?

Explicitly recommends a short window and summary format to manage high-volume data, and explains when to use raw vs summary. This provides clear guidance for the agent across typical use cases.

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