pretrends_equivalence

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

Annotations already declare readOnlyHint=true, so no contradiction. The description adds valuable behavioral context: it reports joint F, non-central-F, and TOST tests, clarifies that a small p-value is the reassuring outcome, and discloses the known limitation that TOST requires tost_threshold with no invented default. Pre-conditions about influence functions and data requirements further enhance transparency beyond 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 longer than ideal but well-structured with labeled sections (Validation, Known limitations, Pre-conditions, Alternatives) and front-loaded with the core concept. Some content, like citations and validation tier, is tangential to invocation, but it remains organized and readable.

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 complex statistical tool with 10 parameters, the description covers purpose, conceptual rationale, limitations, pre-conditions, and alternatives. Combined with a 100%-covered schema and an output schema, an agent has enough context to select and invoke the tool correctly. No critical gaps remain.

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 baseline is 3. The description's mention of tost_threshold largely repeats the schema's note about no universal default, and it does not add new meaning for the other parameters. It provides conceptual framing but no additional parameter-level detail beyond the schema.

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 states a specific verb and resource ('Pre-trend equivalence tests') and clearly distinguishes itself from the conventional pretrends test by explaining it reverses the null and tests for demonstrably small pre-period effects. It names sibling tools as alternatives, making differentiation explicit. The core function is unmistakable.

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 explains when equivalence testing is appropriate (when the conventional test alone overstates evidence due to low power) and lists pre-conditions that must hold for the tool to work. It names alternatives (sp.pretrends_test, sp.pretrends_power, sp.honest_did) but does not map each alternative to a specific scenario, leaving some inference to the agent.

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