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Parse Image File

lyzr_parse_image

Extract text from image files using OCR/VLM, then split content into manageable chunks for downstream processing.

Instructions

Parse an image file (e.g. via OCR/VLM) into text chunks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYesOriginal filename, including extension
mime_typeNoMIME type of the file, e.g. 'application/pdf'
chunk_sizeNoMax characters per chunk (default 1000)
extra_infoNoJSON string of extra metadata (default '{}')
chunk_overlapNoCharacter overlap between chunks (default 100)
parser_configNoJSON string of extra parser configuration
file_content_base64YesBase64-encoded file content to parse
Behavior3/5

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

Annotations already indicate non-read-only and non-destructive behavior. The description adds modest context by mentioning OCR/VLM and 'text chunks,' but it does not disclose potential side effects, resource costs, or whether parsing is stateless. This is adequate given the annotation coverage but not rich.

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, well-formed sentence that front-loads the core purpose. Every word contributes meaning, with no repetition or 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?

Despite having 7 parameters, the schema fully documents each one, and the output schema is absent so no return-value explanation is needed. The description sufficiently captures the tool's role ('image file' + 'text chunks') and the explicit mention of OCR/VLM provides enough behavioral context for an agent to select and invoke it correctly.

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?

Since the schema covers 100% of parameters, the baseline is 3. The description adds value by clarifying that the input must be an image file (not just any file) and that the output will be text chunks, which complements the generic schema descriptions for file_content_base64 and chunking parameters.

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 verb ('Parse'), the resource ('image file'), and the outcome ('into text chunks'), making it immediately distinguishable from sibling parse tools for other formats. The mention of 'OCR/VLM' further specifies the mechanism without ambiguity.

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 context that this tool is for image files, implicitly distinguishing it from parsing tools for PDFs, Word documents, text files, etc. However, it does not explicitly list alternatives or say 'use X instead for non-image files,' so it stops short of full explicit guidelines.

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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