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ai_action

Execute UI actions on Android devices using natural language commands to automate testing and interaction workflows.

Instructions

Perform an action on the UI of the android box (natural language instruction).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
boxIdYesID of the box
instructionYesDirect instruction of the UI action to perform, e.g. 'click the login button'
backgroundNoContextual background for the action, to help the AI understand previous steps
includeScreenshotNoWhether to include screenshots in the action response (default false)
outputFormatNoOutput format for screenshot URIs (default 'base64')
screenshotDelayNoDelay after performing the action before the final screenshot, e.g. '500ms'
Behavior3/5

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

Annotations provide readOnlyHint=false and openWorldHint=true, indicating it's a mutable, flexible operation. The description adds that it performs actions on the UI, which aligns with annotations but doesn't disclose additional behavioral traits like potential side effects, error handling, or performance implications beyond what annotations cover.

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?

The description is a single, efficient sentence that front-loads the core purpose. It avoids unnecessary details, though it could be slightly more structured by explicitly mentioning key parameters or context.

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

Completeness3/5

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

Given the complexity (6 parameters, mutable operation) and lack of output schema, the description is minimal. It covers the basic action but doesn't explain return values, error cases, or how it integrates with sibling tools, leaving gaps for an AI agent to infer usage.

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 description coverage is 100%, so the schema fully documents all 6 parameters. The description mentions 'natural language instruction' which hints at the 'instruction' parameter but doesn't add meaningful semantics beyond what the schema provides, such as examples of complex actions or parameter interactions.

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 clearly states the verb ('perform an action') and resource ('UI of the android box'), specifying it's a natural language instruction. It distinguishes from siblings like get_screenshot (read-only) or install_apk (specific operation) by focusing on general UI interaction, though it doesn't explicitly contrast with open_app which is more specific.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. It mentions 'natural language instruction' but doesn't specify scenarios where this is preferred over more specific tools like open_app or get_screenshot, nor does it mention prerequisites or exclusions.

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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