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analyze_image

Analyze an image from URL, file, or base64 with a natural language query. Get detailed AI analysis for object detection, scene description, and text extraction.

Instructions

Analyze an image using Kolosal Vision AI. Provide an image (URL or base64-encoded) and a description of what you want to know about it. The tool will return a detailed analysis based on your query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesThe image to analyze. Can be a URL (http:// or https://) that will be downloaded, a local file path, or base64-encoded image data. When the user references an image with @ (e.g., @path/to/image.png), use the resolved file path directly.
descriptionYesWhat you want to analyze or check in the image. Examples: 'What objects are in this image?', 'Describe the scene in detail', 'Extract any visible text', 'What is the main subject?'
Behavior3/5

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

No annotations are provided, so the description carries the burden. It mentions the output ("will return a detailed analysis") but does not disclose side effects, limitations, or processing details beyond what the schema already provides. This is acceptable but minimal for a read-only analysis tool.

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 three sentences, front-loading the purpose. The final sentence "The tool will return a detailed analysis based on your query" is somewhat redundant with the first and second sentences, but the overall structure is compact and easy to scan.

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 two-parameter tool with no output schema, the description adequately explains what the tool does and how to invoke it. It lacks details about return format, error handling, or size limits, but these are not critical for basic usage.

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 description coverage is 100%, so the baseline is 3. The description repeats that the image can be a URL or base64, but does not add meaningful semantics beyond the schema's existing descriptions of 'image' and 'description'. No extra value over the structured data.

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

Purpose4/5

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

The description clearly states the tool's function: "Analyze an image using Kolosal Vision AI." It specifies a concrete verb ('analyze') and resource ('image'). Since there are no sibling tools to differentiate against, it cannot fully reach a 5 but is 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 provides clear usage instructions: "Provide an image (URL or base64-encoded) and a description of what you want to know about it." It implicitly tells when to use the tool (when image analysis is needed) but does not mention alternatives or 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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