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

Beyond the readOnlyHint annotation, the description reveals an important side effect: the first call triggers an OAuth authorization prompt. It also clarifies that probe temps measure air around the dough, not the dough core, and lists what will be returned, giving the agent a solid behavioral model.

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 compact but information-dense: it front-loads the core purpose, then adds the auth side effect, entitlement requirement, and return contents. Every sentence contributes new, actionable information without repetition or filler.

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?

Despite having no output schema, the description enumerates the returned fields (ids, flour, hydration, schedule, calculator session link, probe temps) and covers the two key contextual constraints: OAuth authorization and Pro entitlement. This is sufficient for an agent to decide when and how to invoke the 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?

The input schema is empty and parameter count is 0, so there are no parameter semantics to explain. The description instead focuses on return value details, which is appropriate; baseline 4 applies for zero-parameter tools.

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 specific action ('List'), a specific resource ('the signed-in baker's scheduled and active bakes'), and adds the live temperature-sensor dimension. This clearly distinguishes it from sibling tools like list_recipes, which target recipes rather than bakes.

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 gives clear context for when to call this tool: when the user needs their bakes and live probe readings, and it notes the Pro entitlement requirement and first-call OAuth prompt. It does not explicitly name alternatives or exclusions, but the sibling set makes the intended scope evident.

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