Read raw measurements

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

Annotations already mark the tool readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds meaningful behavioral context beyond that: results are ordered most recent first, the tool returns raw rows rather than aggregates, and the privacy note discloses that returned data becomes part of the conversation sent to the model. No contradiction with 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 front-loaded with the core purpose, then structured into clear usage and exclusions lists, followed by a well-scoped privacy note. Every sentence earns its place; the formatting makes it easy for an agent to scan for the decision-relevant information.

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 read-only, optional-parameter tool with a rich output schema and full schema coverage, the description provides everything an agent needs: what the tool returns, ordering behavior, when to prefer alternatives, and privacy implications. There is no meaningful gap in operational or decision 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?

Schema description coverage is 100%, so the schema fully documents all five optional parameters. The description adds only indirect context (e.g., that results include timestamps and sources) but does not need to explain parameter syntax or semantics since the schema already handles that burden. Baseline of 3 is appropriate.

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 ('Read') with a precise resource ('individual measurement rows'), and immediately distinguishes the tool from the daily_metrics aggregate. It also names the data orientation (most recent first) and what kind of detail it exposes (timestamp/source), making its purpose unambiguous and distinct from sibling tools.

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 explicitly states when to use the tool with a concrete user-intent example, and provides a dedicated 'Do not use this tool when' section that names three sibling alternatives (read_health_data, get_metric_history, read_workout_sessions). This gives an agent clear routing guidance with no inference required.

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