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vision_screenshot

Capture a screenshot and use an Ollama vision model to analyze its contents. Provide a prompt to guide the model's focus.

Instructions

Take a screenshot and analyze it with Ollama vision model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoVision model to usellava
promptNoWhat to analyze or look for in the screenshotDescribe what you see on the screen
Behavior2/5

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

No annotations are provided, so the description must carry the burden of disclosure. It states the action ('screenshot') and the analysis, but it fails to disclose important behavioral traits such as that the screenshot captures potentially sensitive screen content, may require system permissions, and that data is sent to an external Ollama model. There is no mention of side effects or reversibility.

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, concise sentence that directly states the tool's purpose. It avoids unnecessary details and is easy to skim. However, it may be under-specified for a tool with external dependencies, but no wordiness is present.

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?

For a simple tool with 2 optional parameters and no output schema, the description provides a reasonable high-level overview. However, given the lack of annotations, it does not explain behavior like screen capture permissions or model usage, and it does not clearly differentiate from vision_analyze_image. It is acceptable but not thorough.

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?

The schema covers 100% of parameters with descriptions ('model' and 'prompt'), and the description does not add extra semantic context. Since the schema already documents what each parameter does, the description adds no additional meaning beyond stating the overall task. Baseline 3 is appropriate given high schema coverage and no gaps to compensate for.

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 uses a specific verb+resource structure: 'Take a screenshot and analyze it with Ollama vision model.' This clearly distinguishes it from sibling tools like vision_camera (likely camera input) and vision_analyze_image (likely analyzing provided images). The scope is explicit: capture the current screen and process it.

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 context: 'Take a screenshot' indicates it is for analyzing the current screen. However, it provides no explicit guidance on when to use this tool versus alternatives (e.g., when an image already exists), nor does it mention any exclusions or prerequisites. Sibling tool names suggest alternatives, but no direction is given.

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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