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process_clipboard_image

Analyze an image pasted from the clipboard using local Ollama vision models. Describe the image, extract text via OCR, or answer custom questions about it.

Instructions

Process an image from the macOS clipboard. Call this when user pastes an image or sends [Image] without a file path.

IMPORTANT: Call this tool whenever you see [Image 1], [Image 2], or the user pastes an image from clipboard. The primary model has no vision capability — this tool reads the image from clipboard and analyzes it.

Args: task: Processing type - "describe" (describe image), "ocr" (extract text), or any custom question about the image

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNodescribe

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that the primary model has no vision capability and that the tool reads the clipboard image and analyzes it, which is essential context. It doesn't mention edge cases like empty clipboard, but for a read/analyze operation, the core behavior is transparent.

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 compact and front-loaded with the core purpose. The 'IMPORTANT' repetition and Args section are slightly redundant but not bloated. Overall structure is logical and efficient.

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?

For a simple one-parameter tool with an output schema, the description covers purpose, usage triggers, and parameter semantics well. It lacks explicit error behavior (e.g., no image on clipboard) and doesn't name sibling tools for file-based images, but it is sufficient for an agent to invoke it correctly.

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?

The schema provides 0% parameter descriptions, but the description compensates fully by explaining the 'task' parameter: 'describe', 'ocr', or any custom question, plus the default value. This gives practical usage semantics beyond the bare schema.

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 ('process') with a specific resource ('image from the macOS clipboard') and clearly states what it does: reads and analyzes clipboard images. It differentiates from siblings by specifying 'without a file path' and 'paste from clipboard', making the scope unambiguous.

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 gives explicit trigger conditions: 'Call this whenever you see [Image 1]... or the user pastes an image from clipboard.' It also implies when-not by saying 'without a file path', but it does not explicitly name alternative sibling tools for file-based images. This is clear context with a minor omission.

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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