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JesonMrLiu

claude-image-recognition-mcp

by JesonMrLiu

recognize_image

Analyze any image from a local file path, URL, or clipboard using an OpenAI-compatible vision model to get a textual answer to your prompt.

Instructions

Recognize / analyze an image via an OpenAI-compatible vision model. Supports a local file path, an http(s) URL, or the literal 'clipboard'. Returns the model's textual answer to your prompt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesLocal file path | http(s) URL | the literal 'clipboard'
modelNoOverride the configured vision model id
detailNoOpenAI vision detail level (default: high)
promptNoQuestion or instruction about the imageDescribe this image in detail.
max_tokensNoMax tokens for the response
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It discloses that the tool uses an OpenAI-compatible vision model and returns a textual answer, implying an external network call. However, it does not mention error behavior, side effects, or privacy implications of sending images to a remote model, which would be relevant for a tool like this.

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 two sentences, front-loaded with the action, and every sentence conveys necessary information without any fluff. It is a model of concise, effective tool definition.

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?

The tool has 5 parameters and no output schema, but the description explains the core purpose, accepted image sources, and return type. It does not describe the other parameters in detail, but the schema does, and for a moderately complex tool this is adequate and slightly above average.

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 primarily restates the image parameter's allowed formats (local path, URL, clipboard), which adds minimal value over the schema. It does not elaborate on the other parameters beyond what the schema already provides.

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 'Recognize / analyze' plus resource 'an image', and clearly states the supported input formats and return type. It unambiguously identifies the tool as an image analysis tool, even though no sibling tools exist to distinguish from.

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?

There is no explicit when-to-use vs alternatives, but since no sibling tools are provided, the context is clear. The description implies its use for analyzing images via a vision model and lists accepted input sources, giving sufficient guidance for when to choose this tool.

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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