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mario-andreschak

MCP Image Recognition Server

describe_image_from_url

Generate detailed descriptions of images using a public URL. Ideal for analyzing web images, this tool supports optional prompts to guide descriptions and works with any server deployment method.

Instructions

Describe an image from a public URL. Most reliable method for web images.

Best for: Images with public URLs accessible from the internet.
Advantages: Works regardless of server deployment method (local/Docker).
Not for: Local files or images already uploaded to the current conversation.

Args:
    url: Direct URL to the image (must be publicly accessible)
    prompt: Optional prompt to guide the description

Returns:
    str: Detailed description of the image

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNoPlease describe this image in detail.
urlYes
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 of behavioral disclosure. It effectively communicates key traits: the tool requires publicly accessible URLs, works reliably for web images, and handles deployment-agnostic access. However, it lacks details on rate limits, error handling, or authentication needs, which would elevate the score further.

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 well-structured with clear sections (overview, usage guidelines, arguments, returns) and front-loaded key information. Every sentence adds value, such as distinguishing use cases and explaining parameters, with no redundant or verbose content.

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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete, covering purpose, usage, parameters, and return type. However, it could be enhanced with more behavioral details like response format or error cases, though the absence of an output schema makes this less critical.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It clarifies that 'url' must be a 'Direct URL to the image (must be publicly accessible)' and 'prompt' is 'Optional prompt to guide the description', providing essential context not present in the schema's bare property definitions.

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 specific action ('Describe an image') and resource ('from a public URL'), distinguishing it from sibling tools that handle local files or already-uploaded images. It explicitly contrasts with 'describe_image' and 'describe_image_from_file' by specifying the input source.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('Best for: Images with public URLs accessible from the internet') and when not to use it ('Not for: Local files or images already uploaded to the current conversation'). It also mentions advantages ('Works regardless of server deployment method') and implicitly references alternatives through sibling tool names.

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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