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jina-ai

Jina AI Remote MCP Server

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by jina-ai

extract_pdf

Extract figures, tables, and equations from PDF documents using layout detection. Returns base64-encoded images of detected elements with metadata from academic papers or any PDF URL.

Instructions

Extract figures, tables, and equations from PDF documents using layout detection. Perfect for extracting visual elements from academic papers on arXiv or any PDF URL. Returns base64-encoded images of detected elements with metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoarXiv paper ID (e.g., '2301.12345' or 'hep-th/9901001'). Either id or url is required.
urlNoDirect PDF URL. Either id or url is required.
max_edgeNoMaximum edge size for extracted images in pixels (default: 1024)
typeNoFilter by float types (comma-separated): figure, table, equation. If not specified, returns all types.
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 of behavioral disclosure. It describes the core functionality and output format ('Returns base64-encoded images of detected elements with metadata'), but lacks details about error conditions, rate limits, authentication requirements, or what happens with malformed PDFs. The description adds value but leaves behavioral gaps.

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 perfectly front-loaded with the core purpose in the first clause, followed by usage context and output details. Every sentence earns its place with zero wasted words, making it highly efficient and scannable.

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

Completeness3/5

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description covers the core purpose and output format adequately but lacks details about error handling, performance characteristics, or limitations. It's complete enough for basic understanding but leaves operational questions unanswered.

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 the schema already fully documents all 4 parameters. The description mentions 'arXiv paper ID' and 'PDF URL' which aligns with the schema, but adds no additional parameter semantics beyond what the schema provides. The baseline score of 3 is appropriate when the schema does the heavy lifting.

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 specific action ('extract figures, tables, and equations'), the resource ('PDF documents'), and the method ('using layout detection'). It distinguishes from sibling tools by focusing on PDF content extraction rather than searching, reading, or processing other data types.

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 for when to use this tool ('Perfect for extracting visual elements from academic papers on arXiv or any PDF URL'), but does not explicitly state when not to use it or name specific alternatives among the sibling tools for different PDF-related tasks.

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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