show_wellbeing

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds value by disclosing that it renders an interactive MCP app and returns a short text summary alongside the visual view, which the schema alone would not convey.

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 with no filler, front-loading the tool's purpose. There is minor redundancy between 'Prefer this for...' and 'When the user asks about this, prefer calling this tool', but the description remains tight and scannable.

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?

For a single optional parameter with an output schema and safety annotations, the description covers purpose, usage context, and output behavior. Nothing needed to invoke it 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%: the single range parameter has an enum and a default in the schema. The description adds no new parameter meaning, so the baseline of 3 is appropriate.

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 (Show) and resource (energy, mood, stress, soreness) with scope (recent context, wellbeing trend). It clearly distinguishes itself from list_wellbeing by emphasizing the visual/interactive wellbeing view rather than raw rows.

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?

Explicitly directs the agent to prefer this tool for 'how-I-have-been-feeling' and subjective recovery questions, and contrasts it with describing underlying rows in text. This gives clear when-to-use guidance and implicitly names the alternative.

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