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parse_pdf

Extract text, tables, and formulas from PDFs and images. Handles documents, screenshots, photos, and scanned files with OCR and structure recognition.

Instructions

Parse PDF and image files (PDF, JPEG, PNG, etc.) to extract text, tables, formulas, and structure using MinerU vunknown. Supports multiple backends including MLX-accelerated inference on Apple Silicon. Works with documents, screenshots, photos, and scanned images.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
backendNoBackend to use: - pipeline: Fast, general-purpose (recommended for most cases) - vlm-mlx-engine: Fastest on Apple Silicon (M1/M2/M3/M4) - vlm-transformers: VLM model, slower but more accuratepipeline
end_pageNoEnding page number (-1 for all pages)
file_pathYesAbsolute path to the file to parse (supports PDF, JPEG, PNG, and other image formats)
start_pageNoStarting page number (0-indexed)
table_enableNoEnable table recognition
formula_enableNoEnable formula recognition
Behavior3/5

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It mentions supported formats and backend behaviors (e.g., MLX acceleration on Apple Silicon) but does not disclose output format, error handling, or any potential side effects. This is a moderate level of transparency—better than nothing but not comprehensive.

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 front-loaded: two sentences quickly state what the tool does, then add context about backends and use cases. There is no redundant or filler content. The minor oddity of 'vunknown' does not affect conciseness.

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 the tool has six parameters and no output schema, the description covers the core functionality, supported input types, and backend options, which is largely sufficient for an agent to understand the tool's role. However, it does not explain return structure or potential limitations, leaving small gaps. Still, it's fairly complete for a parsing 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?

Schema description coverage is 100%, so all parameters are already fully documented in the input schema. The description adds no new semantic details beyond repeating that PDF/JPEG/PNG files are supported and that tables/formulas are extracted, which are already present in the schema property descriptions. Baseline of 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 tool parses PDF and image files to extract text, tables, formulas, and structure. The specific verb 'Parse' and the listed resource types distinguish it from the sibling tool list_backends, 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 context on when to use this tool—for documents, screenshots, photos, and scanned images—and notes backend options including Apple Silicon acceleration. However, it does not explicitly mention exclusions or compare against list_backends, so it's not a 5, but it offers sufficient guidance.

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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