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

With no annotations, the description carries the full burden and does well: it discloses the algorithm (linear fit over 30 calendar days), the minimum-3-weigh-in precondition, and that results are scoped to the authenticated user. It stops short of saying what happens when the precondition fails (error vs. partial result) or any accuracy caveats.

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?

One dense sentence, front-loaded with the primary output (statistics + projected goal date) and followed by the qualifying details. No filler words.

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 read tool with no output schema, the description adequately covers return content and preconditions. Minor gap: it does not describe behavior for users with fewer than 3 weigh-ins or how statistics are shaped.

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 horizonDays is already fully documented with default and max in the schema. The description adds nothing about it, which is the expected baseline 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 and resource ('Return weigh-in statistics and a trend-based projected goal date') and even names the method (linear fit over the last 30 days). This clearly separates it from listEntries/getToday, which merely surface raw data rather than analytic projections.

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?

Usage is implied rather than stated: it never says 'use this when the user asks for a goal-date projection' or excludes cases. It does supply a useful precondition (min 3 weigh-ins), which hints at when the tool is viable, but there is no explicit comparison to the sibling list/read tools.

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