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WendongAI

1pdf

by WendongAI

get_pdf_chunks

Extract text from PDFs in overlapping chunks for agent-based processing of long documents. Adjust chunk size, overlap, and chunk range to manage memory and context limits.

Instructions

Read PDF text in stable overlapping chunks for long-document Agent workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesAbsolute path to the PDF file
overlapNoOverlapping characters between chunks (default 200)
chunk_sizeNoCharacters per chunk, 50-20000 (default 2000)
max_chunksNoMaximum chunks to return, 1-200 (default 20)
start_chunkNoZero-based first chunk index (default 0)
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose core behavior: reading PDF text in overlapping chunks. The term 'stable' suggests deterministic chunking, but details like error handling, return format, and edge cases are absent. This is adequate 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-structured sentence. It front-loads the essential action ('Read PDF text') and provides useful context without any waste. Every word adds value.

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 absence of an output schema and annotations, the description is somewhat thin. It does not specify the exact return structure (e.g., list of strings vs. objects with offsets) or behavior around chunk boundaries and overlap. However, the tool is relatively straightforward with well-documented parameters, so it is minimally adequate.

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 documents all five parameters. The description adds minimal parameter-specific insight beyond the schema; 'stable overlapping chunks' hints at chunking behavior but does not elaborate on how parameters interact. 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 a specific verb ('Read'), resource ('PDF text'), and adds distinguishing context ('stable overlapping chunks', 'long-document Agent workflows'). This differentiates it from siblings like get_page_text or pdf_search, which serve different purposes.

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 by mentioning 'long-document Agent workflows', implying it is intended for chunked text extraction over large documents. However, it does not explicitly state when to use this tool over alternatives like get_page_text or pdf_search, nor does it mention exclusions.

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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