BatterySnap

Rank models by metric

top_models

Read-onlyIdempotent

Rank device MODELS by any battery metric, aggregating across every device of that model. This is the tool for 'which phone model has the best battery life', 'which models charge fastest', 'which run hottest' — questions that would otherwise need one model_summary call per model.

Ordering is by the per-model MEDIAN of the metric (outlier-resistant); the mean is returned alongside. Use brand to rank within one manufacturer and min_devices to require a minimum sample size.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`brand`	No	Optional lowercase brand filter. When omitted, devices from any brand are ranked together.
`limit`	No	Number of devices to return, 1..100.
`order`	No	'desc' (default) ranks the highest values first, 'asc' the lowest. Pick the direction that matches the question: fastest charging is ac_charge_hours + asc; longest battery life is discharge_hours + desc.	desc
`metric`	Yes	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).
`min_devices`	No	Ignore models backed by fewer devices than this. Defaults to 3 so a single outlier phone cannot win a ranking. Raise it for more robust comparisons.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and idempotentHint=true, so the safety profile is covered. The description adds genuine non-obvious behavior: ranking is by the per-model MEDIAN (outlier-resistant) with the mean returned alongside, and the min_devices default of 3 exists to prevent single-outlier wins.

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

Conciseness4/5

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

Well front-loaded: purpose first, then use-case examples, then ordering behavior, then parameter tips. Every sentence carries information, though it runs a bit long and could trim the restated sibling rationale.

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

Completeness4/5

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

With an output schema present, return values needn't be explained, and annotations cover the read-only/idempotent profile. The description fully covers aggregation method, ordering direction, and the key scoping parameters, leaving little an agent would need to guess.

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

Parameters4/5

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

Schema coverage is 100%, so all five parameters are already documented with format examples. The description still adds value by explaining the intent behind `brand` (rank within one manufacturer) and `min_devices` (minimum sample size for robust comparison), going beyond the schema's field-level text.

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 and resource ('Rank device MODELS by any battery metric') and clarifies scope ('aggregating across every device of that model'), which sharply separates it from device-level siblings like top_devices/device_ranking. It also names model_summary as the per-model alternative it replaces.

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

Usage Guidelines4/5

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

Gives three concrete when-to-use examples ('which phone model has the best battery life', fastest charging, hottest) and points to model_summary as the alternative for single-model lookups. It lacks an explicit 'when not to use this' exclusion, so it stops short of 5.

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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