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

Annotations already declare readOnlyHint=true and openWorldHint=false, but the description goes further: it spells out the exclusion rule (frontstage/backstage/support steps are skipped even if they carry metadata.emotion) and enumerates the returned fields. That is real behavioral context beyond the structured fields, though it says nothing about empty-result or partial-data behavior.

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 sentences, each carrying distinct information, with the core computation front-loaded before the filtering caveat and the return shape. Dense but every clause earns its place; only the parenthetical about orderInPhase is slightly buried.

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, but the description compensates by enumerating the return values (emotionCurve fields, overall averageEmotion, swimlanesUsed, countsByType) and the exclusion/ordering logic. An agent has everything needed to call it and interpret the result.

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 parameters (expectedProjectId, _unused) are already documented at the schema level. The description adds no parameter guidance of its own, so the baseline 3 applies.

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?

Uses a specific verb+resource ('Computes the emotion curve of the journey') and immediately defines the unit of aggregation (average emotion per phase). It also carves out a precise inclusion rule (customer-side steps only), which makes it unmistakable against sibling analysis tools like analyze_canvas or validate_journey.

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?

States its intended consumer ('Serves summary visualizations or a numeric recap'), which gives an agent a clear context for when to reach for it. It does not explicitly name an alternative tool or a when-not-to-use condition, so it stops short of the top band.

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