onboarding_funnel

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

With no annotations, the description carries the full behavioral burden, and it does add interpretive value by explaining that a large median_days_to_value signals friction. However, it never states the return shape, whether csv_path is read-only, or what happens with malformed data, leaving meaningful behavioral gaps.

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 stage chain is front-loaded and immediately establishes what the tool produces, followed by two short interpretive sentences. It is compact and wastes little space, though the metric-interpretation sentence could be tightened.

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 funnel stages effectively describe the output columns in lieu of an output schema, which is a plus. But with no annotations and two undocumented required inputs, an agent still lacks enough to supply csv_path and value_event correctly, so it is only partially sufficient.

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 both parameters are required, yet the description never explains what csv_path expects or what value_event should contain. The phrase 'first value' loosely hints at value_event's role, but neither parameter is actually defined, so the description fails to compensate for the schema gap.

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 names the exact artifact it produces and defines its stages (signup -> first value -> came back -> still active), so an agent knows it computes an onboarding funnel rather than a generic metric. It does not explicitly distinguish itself from siblings like retention_curve or find_aha_moments, but the resource is specific enough to act on.

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?

'Answers where new users leak' implies the use case (diagnosing early-lifecycle drop-off) without stating when to prefer this over retention_curve or find_similar_cases. No exclusions, prerequisites, or alternatives are given, so usage is inferred rather than directed.

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