get_quantitative_data

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

With no annotations provided, the description carries the full burden of disclosing behavior. It adds the important prerequisite that analytics must be generated first, and warns about the need to check status and generate if necessary. The read-only nature is implied by the verb 'get', and the description does not disclose side effects or authorization requirements, but the prerequisite is a valuable behavioral note.

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 a clear purpose statement and uses bullet-like examples and a prominent prerequisite warning. It is a bit longer than necessary but every sentence carries information, and the Args section is minimal and helpful.

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?

Given a simple one-parameter tool and an output schema (not shown but present), the description effectively covers what the tool does, when to use it, relevant contexts, and the critical prerequisite. It does not need to explain return values due to the output schema, and it provides enough guidance for an agent to select and invoke the tool correctly.

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 input schema has 0% description coverage, so the description must explain the parameter. It does so with 'study_id: The unique identifier of the study,' which adds meaningful context (uniqueness) beyond the schema's type/title. For a single required parameter, this is sufficient, though not elaborate.

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 tool gets quantitative metrics for a study, naming specific examples like task completion rates, times, and misclicks. It distinguishes itself from sibling tools such as get_qualitative_insights by explicitly focusing on numbers, and from get_analytics_status by describing the actual data retrieval rather than status checking.

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?

It provides explicit usage scenarios ("Use this when the user wants numbers") and gives concrete example queries. It also mentions the most relevant sections (prototype and conceptTesting) and a clear prerequisite workflow with get_analytics_status and generate_analytics, though it does not explicitly say when NOT to use it relative to other tools.

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