Aggregate across many conversations

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

With no annotations provided, the description carries the full burden. It clearly discloses the tool's main behavior—aggregating scores and producing statistical summaries—and even notes the optional group comparison with effect size. It omits edge cases like validation or handling of multiple groups, but is sufficiently transparent for an analytical tool.

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 two sentences with no waste. It front-loads the core function and then adds a crucial usage hint, earning a perfect score for conciseness.

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 no output schema and no annotations, the description adequately explains what the report contains and how to prepare inputs. It could detail the exact output structure, but the overview is sufficient for selecting and invoking the tool.

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 67%, so the baseline is 3. The description reinforces the 'scores' parameter's source and adds semantic context to 'group_by' (optional comparison with effect size), but it does not significantly expand beyond the schema's existing descriptions.

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 aggregates multiple conversation scores into a single report with specific statistical outputs (per-indicator means, spread, optional effect sizes). It distinguishes itself from sibling tools like compare_conversations by focusing on cohort-level aggregation into a funder-readable report.

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 explicitly tells the user to pass structured score objects from score_conversation, giving a clear context for when to use this tool. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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