List Google Health Data Points

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds useful behavioral context: the kebab-case format for data_type and the effect of privacy_mode='raw'. This goes beyond the annotations without contradiction.

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?

Two sentences, each carrying essential information. No redundant words. Front-loaded with the main purpose, then an important technical detail. Excellent structure.

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 description covers the key parameter nuances (data_type format, privacy_mode, response_format) and pagination hints are implied via page_size/page_token. An output schema exists, so return values need not be detailed. Minor gap: no mention of pagination explicitly, but contextually complete for a list operation.

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 50% (only 3 of 6 parameters have descriptions in the schema). The description compensates by clarifying the format of data_type (kebab-case) and the privacy_mode options, adding value that helps an agent use the parameters correctly.

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 verb 'Query' (or list) and the resource 'detailed data points for a Google Health data type', with specific examples like 'steps, sleep, heart-rate'. It distinguishes from siblings by focusing on listing low-level data points vs. summaries or rollups.

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 basic usage guidance (use kebab-case data types) and mentions that 'raw returns upstream Google Health JSON', but does not explicitly state when to prefer this tool over siblings like daily_summary or daily_rollup, nor when not to use it.

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