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take_screenshot

Capture a screenshot of any monitor and optionally describe its contents using AI vision models like Florence-2 or MiniCPM-V.

Instructions

Capture a screenshot and optionally describe it using Florence-2.

Args: output_path: Path to save the screenshot PNG. If None, saves to a temp file. monitor: Monitor index (0 = all monitors combined, 1 = primary, etc.). describe: If True, also run describe_screenshot on the captured image. model_mode: 'fast' for Florence-2 (default), 'deep' for MiniCPM-V 4.6 (better document understanding).

Returns: Dict with path, width, height, monitor, and optionally regions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monitorNo
describeNo
model_modeNofast
output_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden and covers key behaviors: saving to path, monitor selection, optional description, and model modes. It lacks details on permissions or destructiveness but still provides good transparency.

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 well-structured with a clear first sentence summarizing the tool, followed by bullet-like Args that are easy to parse. It is appropriately detailed without being verbose.

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?

The description covers parameters and return values (path, width, height, monitor, optionally regions). Given the absence of annotations and the presence of sibling tools, it could have provided more context on when to choose this tool over alternatives, but it still meets most needs.

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

Parameters5/5

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

Schema description coverage is 0%, but the description explains all four parameters (output_path, monitor, describe, model_mode) with defaults and behavior, fully compensating for the lack of schema documentation.

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?

The description clearly states the tool captures a screenshot and optionally describes it using Florence-2. It distinguishes itself from sibling tools like describe_screenshot and describe_image by specifying the optional description behavior.

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?

The description implies when to use the tool (capturing and optionally describing) but does not explicitly state when not to use it or provide alternatives. However, it does explain the model_mode options for different use cases.

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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