retention_curve

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

With no annotations, the description carries the full burden, and it does disclose two real behavioral traits: cohort eligibility (only users old enough to reach week N count) and small-sample suppression (weeks with fewer than five such users are dropped, not reported). It still omits file access expectations, permissions, and result shape, so it is only partially complete.

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?

Three short sentences, front-loaded with the metric definition before the cohort rules and the interpretation. Little waste, though 'rather than reported as noise' is slightly expository filler.

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?

There is no output schema and no annotations, so the description must set expectations alone. It does well on metric semantics and data-handling rules but leaves the required csv_path and value_event parameters, and the returned structure, undefined for an agent to guess.

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 0% and all three parameters are undocumented in both schema and description. The phrase 'value event in week N' loosely hints at value_event, and max_weeks maps to week N, but csv_path, expected file format, and the meaning/default behavior of max_weeks are never explained.

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 gives a precise, self-contained definition of the metric: weekly retention as the share of users with a value event in week N. That is specific enough to separate it from funnel or aha-moment siblings, but it never names or contrasts an alternative tool, so sibling differentiation is left implicit.

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?

There is no guidance on when to reach for retention_curve versus onboarding_funnel, history_compare, or compare_benchmark. The interpretive note ('where the curve flattens is the retention that holds') helps read the output but says nothing about when or why to invoke the tool.

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