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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the underlying models (NEAT+EAT vs lookup table), which goes beyond the annotations. No contradictions.

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 a single, well-structured sentence that front-loads the core purpose and methods. Every word contributes value with no 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?

With an output schema and no required parameters, the description covers the main two methods effectively. Could be slightly more complete by explaining how the models differ in terms of input requirements, but overall it provides sufficient context for a specialized tool.

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%. The description explains occupation/steps for non-exercise and training volume for exercise, which maps to some parameters (occupation, steps_per_day, session_minutes, sessions_per_week). However, parameters like bmr, weight, methods, intensity, activity_level are not explained.

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 estimates the TDEE activity multiplier using two specific methods (classic lookup table or NEAT+EAT model). It distinguishes from sibling tools like tdee (which calculates full TDEE) and adaptive-tdee.

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 mentions two models but does not explicitly guide when to use one over the other or when to prefer this tool over siblings. No when-not-to-use guidance is provided.

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