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llt22
by llt22

recognize_image

Analyze images from clipboard, URLs, or files to describe content, read text, and answer questions using a vision model.

Instructions

Recognize and analyze an image using the configured vision model. If no image is provided, reads the current clipboard image. Also supports local file paths, http(s) URLs, base64, data URLs, and the literal "clipboard".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageNoImage to recognize. Accepts: http(s) URL, local file path, data: URL, raw base64 string, or the literal "clipboard". Defaults to "clipboard".clipboard
detailNoVision detail level. 'low' is cheaper and faster; 'high' for fine text.auto
promptNoQuestion or instruction about the image, e.g. 'What text is on this sign?'Describe this image in detail, including any visible text.
maxTokensNoMax tokens for the response. Defaults to 1024.
Behavior2/5

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

With no annotations, the description must disclose all behavioral traits. It mentions reading clipboard if no image is provided and supported input types, but does not describe potential side effects, read-only status, model limitations, or cost implications. The description is insufficient for full behavioral transparency.

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

Conciseness5/5

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

The description is extremely concise with two sentences. The first sentence front-loads the core purpose, and the second adds essential context on input sources. Every sentence contributes value with no wasted words.

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

Completeness2/5

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

Despite having no output schema, the description fails to explain what the tool returns after analysis (e.g., text description). It also does not cover potential limitations, authentication, or rate limits. The description is incomplete for a tool with 4 parameters and no output schema.

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 coverage is 100%, so the baseline is 3. The description reinforces the default clipboard behavior and supported formats already covered in the schema, but adds no new semantic meaning beyond what the schema provides. Thus, it meets the baseline without exceeding it.

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's purpose: recognizing and analyzing images using a vision model, with specific details about input sources. It is a specific verb+resource combination that leaves no ambiguity about the tool's function.

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 by explaining default clipboard behavior and supported formats, but lacks explicit guidance on when to use this tool versus alternatives (none provided) or when not to use it. It provides basic usage context but no 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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