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

Annotations only declare readOnlyHint, and the description adds substantial behavioral context beyond that: the unit of scoring (one data point per prospect-channel, one per prospect when several team members reach the same person), the synthetic 'No match' row appended when anyone is unclassified, and the confidence thresholds behind `signal` ('thin' <10, 'early' <30, else 'solid'). This is exactly the kind of context annotations cannot carry.

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?

Reasonably front-loaded: the summary opens with the verb and resource, the scoring model follows, and return shape is separated into a returns block. It is long but most sentences carry unique information; a couple of illustrative examples could be trimmed.

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?

No output schema exists, yet the returns block fully describes the return dict (group_id, group_name, mode, measure, tags and their fields), explains k/n semantics, and clarifies signal. Nothing an agent needs to interpret or call the tool correctly is missing.

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 coverage is 100%, so parameters are already documented in the schema (including the senders no-fallback rule and channel enum). The description restates agent_ids scoping and the group's campaigns list but adds no syntax or format detail for measure, channel, senders, or on_behalf_of. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb+resource (per-tag outreach outcome rate for a measure over a segment group's classified people) with concrete use cases ('which seniority replies most'). The scoring model described (one data point per prospect-channel) makes it clearly distinguishable from siblings like get_message_tag_rates and get_segment_funnel.

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?

Gives clear usage context: pass agent_ids to scope to campaigns, use list_segment_groups to find a group's campaigns, and query_segment_people(untagged=true) to list the 'No match' people. It does not explicitly contrast itself with the similarly named sibling rate/funnel tools, so the when-to-use-this-vs-that guidance is implicit rather than explicit.

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