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android_screenshot

Capture an Android device screen as a viewable image. Optionally pass a question to analyze the screenshot with a local vision model, returning the answer.

Instructions

Capture the screen and return it as an image you can look at directly.

Args: device: Device serial; defaults to the first physical device. scale: Long-edge px for the returned image (full-res is saved to disk regardless). analyze: Optional question - also runs the shot through the local LM Studio VLM and attaches its text answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scaleNo
deviceNo
analyzeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions that the full-res image is saved to disk, which is useful, and that 'analyze' runs the shot through a VLM. However, it doesn't disclose potential side effects (e.g., does it alter device state? does it require a connected device?), the exact return format (though it says 'image you can look at'), or any latency/rate concerns. This is a gap for a tool with no annotations.

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?

The description is concise and structured with a clear summary sentence then bullet-like parameter info. It front-loads the primary purpose and keeps each line focused. No wasted words.

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?

Given the tool's moderate complexity (3 optional params, no output schema, no annotations), the description covers the essentials: what it does, how to control scale, and the optional analyze feature. It doesn't mention error conditions or return format details, but for a screenshot tool that's acceptable. The save-to-disk behavior is disclosed. It feels nearly complete.

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?

The schema has no descriptions for any parameters (0% coverage), so the description must compensate. It does: 'Device serial; defaults to the first physical device', 'Long-edge px for the returned image (full-res is saved to disk regardless)', and 'Optional question - also runs the shot through the local LM Studio VLM'. These add meaning beyond the schema's type/default.

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?

The description states a clear verb ('Capture') and resource ('the screen'), and mentions it returns an image. It distinguishes itself from siblings like android_ui_dump (which dumps UI hierarchy) and android_record_start (which records video), though it doesn't name alternatives explicitly. It could be more specific by contrasting with these siblings.

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

Usage Guidelines3/5

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

The description implies usage: it's for capturing a screenshot for direct visual inspection. It mentions optional parameters like scale and analyze, but doesn't explicitly say when to use this vs alternatives (e.g., when you need to see visual state vs UI hierarchy). No exclusions are provided.

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