ollama-vision-mcp

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ocr_image

Extract visible text from images via OCR. Supports language hints for English, Vietnamese, Japanese, Chinese, and Korean.

Instructions

Extract all visible text from an image (OCR).

Args: image_path: Absolute path to the image file language: Primary language hint - "auto", "vi", "en", "ja", "zh", "ko"

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": {
      "image_path": "<image_path>"
    },
    "name": "ocr_image"
  }
}

Input Schema

Name	Required	Description	Default
`language`	No		auto
`image_path`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 2, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the behavioral transparency burden. It only states the core action and language parameter, but omits details such as supported image formats, error behavior, limitations, or permission requirements. This leaves the agent with little beyond the raw function purpose.

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 compact and front-loaded: one sentence for the purpose plus a short Args list. Every sentence adds useful information and there is no filler.

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 OCR tool with an output schema present, the description covers the primary behavior, arguments, and language scope. The main gap is the lack of explicit guidance on when to choose this tool over sibling image tools, but overall it is sufficiently complete for the tool's complexity.

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

Parameters4/5

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

The input schema has 0% description coverage, but the description compensates well by explaining image_path as an absolute path and language as a hint with explicit supported values. It does not mention that language is optional or how auto-detection interacts, so it is not perfect.

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 and resource: 'Extract all visible text from an image (OCR)'. This clearly distinguishes it from sibling tools like describe_image or ask_image, which serve different purposes.

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 use when text extraction from images is needed, but it never explicitly states when to use this tool versus sibling tools, nor does it mention any exclusions or alternatives. The absence of explicit guidance makes it only minimally useful.

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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