BatterySnap

List models

models

Read-onlyIdempotent

Return the mobile devices models of the given brands for which we have battery statistics, along with the brand each model belongs to, the number of devices recorded, the average battery temperature in Celsius, and the min/max Android SDK level per model.

Each entry is grouped by the resolved street name (e.g. 'Galaxy S23'), which may cover several raw Android Build.MODEL identifiers (carrier variants, regional SKUs, …). For URL building, every entry also exposes:

raw_model_code: a representative raw Build.MODEL identifier (the lexicographically smallest one among devices grouped under this street name), or null if no device reported a non-empty model code.
url: the public BatterySnap web page for the model, e.g. https://www.battery-snap.com/brands/samsung/models/SM-S911B. Built from brand + raw_model_code; null when either is null; raw_model_code is null. The page lists all devices reporting that exact raw model.

Pass min_device_count / max_device_count to filter models by how many devices were recorded. For 'which models have the most devices' questions, pass sort='device_count' with a small limit (and typically fields=['brand', 'model', 'device_count', 'url']) instead of fetching and sorting the full list. Pass fields to keep only specific attributes in each entry. Omit brands (or pass an empty list) to return models across every brand.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sort`	No	Sort order of the returned models: 'name' (default, alphabetical by brand then model) or 'device_count' (most recorded devices first). Combine sort='device_count' with `limit` to answer 'most popular models' questions in one small call.	name
`limit`	No	Return at most this many models (applied after filtering and sorting). Omit for no cap.
`brands`	No	One or more lowercase Android device brand identifiers (e.g. ['samsung', 'xiaomi']) to fetch models for. Matched case-insensitively. Omit (or pass an empty list) to return models across all brands.
`fields`	No	Optional whitelist of attribute names to keep in each returned entry, used to shrink the response. When omitted or empty, all attributes documented in the output schema are returned. Unknown names are silently ignored.
`offset`	No	Skip this many entries before returning results — use with `limit` to page through a long list. Defaults to 0.
`max_device_count`	No	Drop entries with more than this many recorded devices. Use to skip very common entries. Omit for no upper bound.
`min_device_count`	No	Drop entries with fewer than this many recorded devices. Defaults to 1 (keep all).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, openWorldHint=false), and the description adds genuinely non-obvious behavior: grouping by resolved street name with several raw Build.MODEL ids collapsed, lexicographically-smallest representative raw_model_code, url nullability rules, and that unknown field names are silently ignored.

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

Conciseness3/5

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

Front-loaded and mostly useful, but it restates return attributes that the output schema already carries, and one clause is malformed ('null when either is null; `raw_model_code` is null'), which adds noise rather than clarity.

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?

For a 7-parameter read tool with a full output schema, the description supplies the caveats an agent needs (grouping, nullability, filtering/paging) without over-explaining. Return-value detail is partly redundant with the output schema, keeping it just below a 5.

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 the baseline is 3; the description goes further by prescribing the composed pattern (sort='device_count' + small limit + fields instead of fetching and sorting the whole list) and explaining the min/max_device_count filtering intent beyond the literal schema wording.

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

Purpose4/5

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

States a precise verb+resource ('Return the mobile devices models of the given brands') and enumerates the attributes returned, so the agent knows exactly what it gets. It never explicitly contrasts with the nearby siblings (model_summary, top_models), so the differentiation is implicit rather than stated.

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 concrete usage patterns: pass min/max_device_count to filter, combine sort='device_count' with a small limit for 'most devices' questions, use fields to shrink entries, omit brands to span all brands. It stops short of saying when to prefer a different sibling tool instead of this one.

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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