bosch_battery_trends

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

With no annotations supplied, the description carries the full burden, and it does disclose non-obvious behavior: that values are aggregated per period with averages and deltas, and it provides interpretation semantics (declining remaining_energy_wh at constant charge level indicates capacity degradation). It omits whether the call is read-only, any data-availability preconditions (e.g. needing a prior sync), and the shape of the returned structure.

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?

Front-loads purpose, then a tight bulleted list of computed metrics, then interpretation guidance, then args — a clear scan order with no filler prose. It is on the longer side and duplicates the enum/default already in the schema, but nearly every line adds information an agent would otherwise have to guess.

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 four-parameter, all-optional read tool with no annotations and no output schema, the definition covers what the tool computes, how to interpret each metric, and the meaning and accepted formats of every argument. The only residual gap is the concrete response structure, which is largely implied by the 'per-period averages and deltas' framing.

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 0%, so the description must compensate, and it does: it explains bike_id as an optional UUID with an all-bikes fallback, describes period as the aggregation period with its default, and — crucially — gives accepted start_date formats ('YYYY-MM-DD, YYYY-MM, Nd, or None for all data') plus the end_date default of today, none of which appear in the schema. It repeats the period enum already present in the schema, which is minor redundancy.

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 ('Analyse battery health trends for your Bosch eBike over time') and enumerates exactly what is computed (charge cycle count, average battery level, lifetime kWh, remaining energy trend), which separates it from point-in-time siblings like bosch_get_soc or bosch_get_capacity. It does not name those siblings explicitly, so differentiation is by inference rather than routing.

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?

Includes one usage cue — 'Compare early and recent snapshots for a long-term health picture' — which implies the tool is for longitudinal analysis rather than a single reading. However, it never states when NOT to use it or points to the alternative tools (bosch_get_capacity, bosch_get_soc) for instantaneous values, leaving the choice to the agent.

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