suggest_next_weight

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

With no annotations, the description carries the full burden. It explicitly states 'This is read-only (it does not change anything on Garmin)' and explains the double-progression rule, which adds valuable context. However, it does not disclose the output format, error behavior (e.g., missing history), or any other side effects, leaving notable gaps.

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 efficient: three introductory sentences covering purpose, method, and read-only nature, followed by a clean list of parameters. No filler or redundancy.

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?

The tool has 5 parameters, no annotations, and no output schema. The description explains the methodology and parameters well, but fails to describe what the output looks like (just 'recommend' with no structure) or edge cases (e.g., no history found). This makes it incomplete for an AI agent needing to handle responses robustly.

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 0%, so the description must compensate. It explains each of the 5 parameters with meaningful context: exercise_name is a 'case-insensitive substring' with examples, reps_low/high define the 'target rep range', increment_kg is a 'weight jump', and weeks is 'how many weeks of history'. This goes far beyond the schema's types and defaults.

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 states a clear verb+resource: 'Recommend the next weight & reps for a lift using progressive overload.' It distinguishes itself from siblings like get_strength_history (reads history) and create_strength_workout (creates workouts) by focusing on recommendation logic.

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 clearly implies when to use the tool: when you need a weight/reps suggestion based on progressive overload and recent history. It notes it reads history, making the context clear. However, it does not explicitly name alternatives or state when not to use it, so it falls 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.