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mimo-vision-mcp

mimo-vision-mcp

by mikulovesuki
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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extract_text_from_image

Extract text from images via OCR, turning screenshots and photos into searchable, editable content.

Instructions

提取图片中的文字（OCR，基于视觉模型）。

当用户要求"识别/提取图中文字、截图里的文本"时调用。输入约定同 analyze_image。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "images": "<images>"
    },
    "name": "extract_text_from_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
detailNo
imagesYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds that it's 'OCR based on vision model', which gives some insight into how it works. However, it does not disclose potential limitations, error behavior, or whether images are stored, leaving some behavioral aspects unspecified. The read-only nature mitigates the gap, but the description is thin.

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, two sentences, with the main purpose front-loaded. Every sentence adds value, and there is no wasted wording. The reference to analyze_image is efficient, though it creates a dependency.

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?

An output schema is present, so return values are covered externally. The description covers purpose and usage trigger. The only gap is that it relies on the sibling tool for input conventions, but given the tool's simplicity and the presence of sibling context, it is adequately complete for an OCR tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It only says 'input conventions same as analyze_image' without explaining what images or detail mean in practice. This is a pointer to sibling information rather than an explanation, failing to provide direct parameter semantics for the agent.

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 extracts text from images via OCR with a specific verb and resource. It also distinguishes from sibling tools by explicitly mentioning when to use it for 'recognize/extract text in images, screenshots', which sets it apart from analyze_image and describe_image.

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 an explicit trigger condition: 'Call when user asks to recognize/extract text in images'. It also references input conventions from analyze_image, giving clear context. However, it does not explicitly mention when not to use this tool, though the sibling distinction is implicit.

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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