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Extract Text from Image (OCR)

ai.ocr.extract
Read-onlyIdempotent

Extract text from images and PDFs using OCR. Supports 20+ languages including English, Chinese, Japanese, Korean. Accepts PNG, JPG, GIF, BMP, PDF, TIFF.

Instructions

Extract text from any image or PDF URL using OCR — supports 20+ languages including English, Russian, Chinese, Japanese, Korean, Arabic. Returns recognized text. Handles PNG, JPG, GIF, BMP, PDF, TIFF (OCR.space)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the image or PDF to extract text from (PNG, JPG, GIF, BMP, PDF, TIFF supported)
languageNoOCR language: "eng" (English, default), "rus" (Russian), "ger" (German), "fre" (French), "spa" (Spanish), "jpn" (Japanese), "kor" (Korean), "chs" (Chinese Simplified)
filetypeNoFile type hint — set if URL has no extension or content-type is wrong
detect_orientationNoAuto-detect and correct image orientation (default false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint, and the description adds that it returns recognized text and handles multiple formats. It goes beyond annotations by mentioning the backend (OCR.space), which is useful for understanding behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that packs essential information concisely. No fluff, but it could be slightly more structured (e.g., bullet points for languages). Still, it earns its place.

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 an output schema (assumed to explain return format), and the description explicitly says 'Returns recognized text'. With annotations covering safety and idempotency, the description is sufficiently complete for an OCR extraction tool.

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?

All parameters are described in the schema (100% coverage), so the description adds minimal new semantics. It broadly mentions language support and file types, but the schema already provides details. Baseline 3 is appropriate.

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 action 'Extract text' and the resource 'any image or PDF URL'. It also lists supported languages and file types, making it unambiguous. Among siblings, no other tool performs OCR, so it is distinct.

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?

No explicit guidance on when to use this tool vs alternatives, likely because it is the only OCR tool. However, it does not state when not to use it or mention any prerequisites, which would be helpful.

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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