bosch_battery_trends

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It details what the tool computes (per-period averages and deltas, charge cycle accumulation, energy trends) and interprets the significance of a declining remaining_energy_wh. It does not mention auth requirements or explicitly state read-only behavior, but the analytical framing and focus on computed trends communicate the non-mutating nature.

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 well-structured with a concise opening, bulleted metric list, and an Args section. Every line provides operational or interpretive value—no filler or redundancy—and the format makes it easy for an agent to parse key facts quickly.

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 that an output schema exists, the description does not need to explain return values. It covers the aggregation periods, filters, metrics, and how to interpret degradation, making the tool's behavior and purpose fully clear. It is complete for a read-only analytical tool with moderately sophisticated parameters.

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 fully document the parameters, and it does. It explains all four parameters: bike_id (optional, all bikes if omitted), period (valid aggregation options and default), start_date (allowed formats and default), and end_date (default today). This adds substantial meaning beyond the bare schema definitions.

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 opens with a specific verb, 'Analyse battery health trends for your Bosch eBike over time,' and enumerates the exact metrics computed. This clearly distinguishes it from point-in-time sibling tools like get_soc and get_capacity by focusing on temporal aggregation and degradation analysis.

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 establishes when to use the tool: for long-term health analysis, comparing early and recent snapshots, and identifying capacity degradation over time. It does not explicitly name alternative tools or state when not to use it, but the contextual guidance is strong enough to guide selection.

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