analyze_screen

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

In the absence of annotations, the description thoroughly discloses the tool's behavior: it dumps the hierarchy, classifies modules, applies noise filtering, and requires specific operational conditions. It also explains the launching behavior with clearState: false. This provides good transparency beyond the schema.

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

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

The description is a single paragraph but packs substantial information efficiently. It is front-loaded with the main purpose and then details. While slightly dense, it avoids unnecessary verbosity and is structured logically.

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

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

Given the tool's complexity and the lack of an output schema, the description is highly complete. It explains what the tool returns (hierarchy, classified modules, candidate_tcs), lists prerequisites, and covers optional behaviors comprehensively. No significant gaps remain.

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

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

The input schema has 100% coverage with descriptions for all three parameters. The description adds significant context: app_id is used for launching or labeling, launch_app details the exact behavior (clearState: false), and timeout_ms adjusts for BlueStacks. This goes beyond what the schema provides.

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

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

The description clearly identifies the tool as a mobile version of analyze_url, specifying that it dumps the view tree of the current foreground app on a mobile device, classifies UI elements into forms, CTAs, and tab bars, and filters noise. It differentiates from the sibling analyze_url by explicitly stating 'Mobile 版的 analyze_url'.

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

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

The description provides clear context for when to use the tool, including prerequisites (Maestro CLI installed, device booted, app in foreground) and options to launch the app. It does not explicitly mention when not to use it or list alternatives, but the context is sufficient for selecting the tool.

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