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chupre
by chupre

recognize_text

Extract text from images (JPEG/PNG) or single-page PDFs using Yandex Vision OCR. Supports printed, handwritten, table, and markdown models with language selection.

Instructions

Recognize text in an image (JPEG/PNG) or a single-page PDF using Yandex Vision OCR (synchronous). Picks the recognition model via 'model' (default 'page' for printed text; 'handwritten', 'table', 'markdown', etc.). Provide a local file 'path' or 'base64' content. For multi-page/large PDFs, use recognize_pdf instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoAbsolute or relative path to a local file to read and OCR. Provide this OR 'base64'.
base64NoBase64-encoded file content. May be a data URI ('data:<mime>;base64,...'). Provide this OR 'path'.
mimeTypeNoExplicit MIME type override (e.g. 'image/jpeg'). Inferred from the file when omitted.
languagesNoRecognition languages (selectable). Use ['ru'], ['en'], or ['ru','en'] for mixed text. Defaults to ['ru']. This Yandex OCR endpoint does not support auto-detect.
modelNoRecognition model. 'page' (default): single-column printed text. 'page-column-sort': multi-column text. 'handwritten': mixed handwritten + printed (ru/en). 'table': tables (ru/en). 'markdown': printed text with Markdown output. 'math-markdown': math formulas (LaTeX in Markdown).page
formatNoOutput format. 'text' (default) returns recognized plain text. 'markdown' returns the model's Markdown output when available (markdown/math-markdown models), otherwise plain text. 'json' returns the full textAnnotation (blocks/lines/words/tables/markdown).text
Behavior4/5

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

With no annotations, the description carries full burden. It discloses synchronous operation, supported MIME types, model behaviors, and language limitations (no auto-detect, only ru/en). It could mention error handling or response size, but overall transparency is good.

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 a single efficient paragraph of 3-4 sentences, front-loaded with core purpose. Every sentence adds value: format support, model options, input alternatives, and sibling tool reference. No wasted words.

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?

Given 6 parameters, 100% schema coverage, and no output schema, the description covers input sources, model options, language limits, and sibling tool. It omits error handling and output format details (but format parameter covers that). Adequate for the tool's simplicity.

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 coverage is 100%, so baseline is 3. The description adds context about model use cases and mutual exclusivity of path/base64, but does not significantly deepen understanding beyond schema.

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's purpose: recognize text in images or single-page PDFs using Yandex Vision OCR (synchronous). It specifies supported formats and distinguishes from the sibling tool 'recognize_pdf' by noting multi-page PDFs should use that tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool vs 'recognize_pdf' for multi-page/large PDFs. It also explains input options (path vs base64), model selection, and output formats, giving clear usage context.

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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