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Parse CSV File

lyzr_parse_csv

Parse CSV files into text chunks by specifying the column to extract as source text. Convert structured tabular data into usable text segments for downstream AI processing.

Instructions

Parse a CSV file into text chunks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYesOriginal filename, including extension
mime_typeNoMIME type of the file, e.g. 'application/pdf'
extra_infoNoJSON string of extra metadata (default '{}')
source_columnYesName of the column to use as the source text
file_content_base64YesBase64-encoded file content to parse
Behavior2/5

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

Annotations provide minimal safety context (readOnlyHint=false, destructiveHint=false), but the description adds no behavioral detail beyond 'text chunks'. It does not disclose chunking behavior, size limits, how rows or columns are handled, or whether parsing has any side effects. No contradiction with annotations, but the description carries little additional transparency.

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 sentence with no filler. It front-loads the action and result, making it easy for an agent to quickly understand the tool's core function. Every word earns its place.

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?

For a simple parse tool with a fully described schema, the description is adequate but has gaps. It lacks details about what 'text chunks' means, how source_column influences the output, and what the return value looks like. The absence of an output schema makes these details more relevant, but the tool is simple enough that the description is minimally viable.

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?

The input schema has 100% description coverage for all five parameters, including source_column ('Name of the column to use as the source text'). The description does not add parameter-specific meaning beyond what the schema already provides, so a baseline score 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 uses a specific verb ('Parse'), a specific resource ('CSV file'), and an outcome ('text chunks'), clearly distinguishing it from sibling parse tools for PDF, DOCX, TXT, XLSX, PPTX, and images. The title and description align, leaving no ambiguity about what the tool does.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives. The sibling list contains many parse_* tools, but the description does not mention why one would choose CSV over XLSX, or how source_column affects usage. There are no prerequisites, exclusions, or alternative tool references.

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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