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Parse PDF File

lyzr_parse_pdf

Parse a PDF file into text chunks.

Instructions

Parse a PDF file into text chunks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
s3_keyNoS3 object key, if sourced from S3
filenameYesOriginal filename, including extension
mime_typeNoMIME type of the file, e.g. 'application/pdf'
s3_bucketNoS3 bucket name, if sourced from S3
chunk_sizeNoMax characters per chunk (default 1000)
extra_infoNoJSON string of extra metadata (default '{}')
data_parserNoParser strategy to use, e.g. 'simple'
chunk_overlapNoCharacter overlap between chunks (default 100)
parser_configNoJSON string of extra parser configuration
file_content_base64YesBase64-encoded file content to parse
Behavior2/5

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

Although annotations include readOnlyHint=false and destructiveHint=false, the description adds no behavioral context beyond 'text chunks'. It does not disclose whether the operation has side effects, whether results are stored, what the chunking output looks like, or any limitations (e.g., scanned PDFs). The description is purely declarative and does not enhance the annotation-provided safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no fluff. It is concise and easy to scan, but it is somewhat under-specified for a tool with 10 parameters. Nonetheless, every word is useful, and it is not verbose.

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

Completeness2/5

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

Given the 10 parameters and lack of an output schema, the description is too sparse. It does not explain the structure of the returned chunks, how to choose between file_content_base64 versus S3 source, default chunking behavior, or how this tool relates to KB training tools. An agent would need to infer or inspect the schema to use it effectively.

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 baseline is 3. The description itself does not mention any parameters or their semantics, but the schema already documents all 10 parameters with their descriptions. Thus, the description adds no extra value beyond the structured definitions.

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 'Parse a PDF file into text chunks' uses a specific verb and resource, clearly indicating the tool's function. It distinguishes itself from sibling parse tools for other formats like docx, csv, xlsx, and image by naming 'PDF file' explicitly.

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

Usage Guidelines3/5

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

Usage is implied by the tool name and description: it is for parsing PDFs. However, there is no explicit guidance on when to use it versus the many other parse tools, nor any mention of prerequisites, such as how to provide the file (base64 vs S3) or when to set parser_config. The description does not prevent misuse but offers minimal direction.

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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