low-hallucination-vision

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ocr_extract

Extract visible text from images using OCR. Returns bounding boxes and confidence scores; unclear characters are dropped, never guessed, to reduce false claims.

Instructions

Extract visible text from an image (OCR only, no scene description).

Args:
    image_source: Local file path or http(s) URL of the image.
    prompt: Optional extra instructions.
    temperature: Sampling temperature, default 0.2.

Returns:
    JSON: {"texts":[{"text","bbox","confidence"}], "overall_confidence"}.
    Unclear characters are dropped, never guessed.

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_source": "<image_source>"
    },
    "name": "ocr_extract"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	No
`temperature`	No
`image_source`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals key behaviors: it returns a specific JSON structure, and unclear characters are dropped rather than guessed. This adds meaningful context beyond a bare description, though it does not cover potential rate limits or authentication requirements.

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 concise and well-structured. The first line states the core purpose, followed by an 'Args' section and a 'Returns' section. Every sentence adds value—no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters and an output schema, the description is remarkably complete. It includes parameter semantics, return format, a behavioral caveat about OCR accuracy, and the scope limitation. This allows an agent to select and invoke the tool correctly without additional context.

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 schema provides only parameter names and defaults, but the description explains each parameter's semantics: image_source can be a local path or HTTP(s) URL, prompt is optional instructions, and temperature is sampling temperature with a default of 0.2. This compensates well for the 0% schema coverage.

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 visible text from images, with the explicit constraint 'OCR only, no scene description'. This distinguishes it from sibling tools like analyze_image and detect_elements, making its purpose 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 use context: it is for OCR text extraction, not scene understanding. It explicitly excludes scene description, which helps the agent avoid using this tool for image analysis tasks. However, it does not name sibling alternatives directly, so a 4 is appropriate.

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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