show_body_composition

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

The annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds transparency about the tool's behavior beyond that: it 'renders an interactive MCP app' and 'returns a short text summary alongside the visual view.' These are useful behavioral details that the annotations do not capture, and they do not contradict any annotation.

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 long and efficiently front-loads the core purpose. The first sentence states what is shown and lists metrics; the second provides usage guidance and output format. Every clause contributes meaningful information—there is no fluff, redundancy, or excessive detail.

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 the tool's simplicity (one optional parameter with an enum, no nested objects), the description covers all essential context: what the tool displays, which metrics are included, when to use it, and what the output looks like (visual app plus text summary). It is complete enough for an agent to decide when and how to call it, though the actual output schema is not detailed in the description itself.

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?

The schema has 100% coverage: the only parameter 'range' has a description ('Time window. One of 7d, 30d, 90d, 1y. Default 90d.') and an enum. The tool description adds no additional parameter-specific information beyond what the schema already provides. Since the schema fully documents the parameter, a baseline score 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?

The description clearly states the tool's purpose: 'Show body composition over time with an interactive metric picker' and enumerates the specific metrics (weight, body fat, lean and muscle mass, hydration, visceral fat, BMI, waist, and related scale metrics). It uses a specific verb ('show') and identifies the resource ('body composition'), leaving no ambiguity about what the tool does.

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?

The description provides explicit usage guidance: 'When the user asks about this, prefer calling this tool and rendering the interactive MCP app over describing the underlying rows in text.' This tells the agent when to invoke the tool and contrasts it with an alternative (text descriptions). It could be more explicit about other alternatives (e.g., raw data tools like list_body_metrics), but the condition and preference are clear.

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