BatterySnap

Compare a device to its peers

device_ranking

Read-onlyIdempotent

Answer 'is MY phone normal?' — rank one device's metric against other devices of the same model AND against the whole fleet, in one call. Returns the device's value, its 1-based rank, its percentile, and the cohort median / mean / min / max for both cohorts.

Prefer this over fetching device_summary + model_summary and comparing by hand: it is a single call and gives the percentile directly. The model cohort is the meaningful one for 'is this device degraded'; the fleet cohort puts the model itself in context.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`metric`	No	Which battery metric to rank on. One of: 'temperature_c' (mean battery temperature, C), 'max_temperature_c' / 'min_temperature_c' (hottest / coldest ever seen, C), 'battery_level_pct' (mean charge level, 0-100), 'discharge_hours' (hours to drain a full 100% - HIGHER means longer battery life), 'ac_charge_hours' / 'usb_charge_hours' / 'wireless_charge_hours' (hours to charge a full 100% on that source - LOWER means faster charging), 'awake_ratio' (fraction of time awake / screen-on, 0..1), 'snapshot_count' (how much data the device contributed), 'uptime_hours' (longest single uptime between reboots).	discharge_hours
`device_uid`	Yes	Stable device UUID returned by `devices`, `top_devices` or the share flow.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	No	Public BatterySnap page with this device's interactive battery history.
`brand`	No	Lowercase Android brand identifier.
`fleet`	No	Rank against every device in the fleet, all models mixed.
`model`	No	Resolved model name (street name).
`value`	No	This device's value for the metric.
`metric`	No	The metric that was ranked.
`raw_model`	No	Raw Build.MODEL identifier.
`device_uid`	No	The device that was ranked.
`model_cohort`	No	Rank against other devices of the SAME brand + model — the fair like-for-like comparison.
`snapshot_count`	No	Snapshots behind this device's aggregate.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: what each of the two cohorts means and which one answers the degradation question, which the annotations cannot convey. It stops short of stating cost, rate limits, or minimum sample-size caveats for sparse cohorts.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the user question it answers, then the return contents, then the routing advice — no wasted sentences. Every clause either defines scope, describes output, or steers tool selection.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with full schema coverage and an output schema, the description covers the remaining gaps: cohort semantics, the routing alternative, and what the call returns. Nothing an agent needs in order to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% — the schema already documents both parameters in depth, including every metric enum value and its unit/direction. The description adds only that ranking is against same-model and fleet cohorts, not new per-parameter meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (rank) and resource (one device's battery metric) and pins the scope precisely: ranked against same-model peers AND the whole fleet in one call. It explicitly differentiates itself from siblings device_summary and model_summary, so an agent can select it without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use rule ('Prefer this over fetching device_summary + model_summary and comparing by hand') and names the concrete alternative plus the reason (single call, percentile computed directly). It also advises on cohort selection — the model cohort answers 'is this device degraded', the fleet cohort contextualizes the model itself.

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

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