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senoff

xlsx-for-ai

xlsx_schema

Read-onlyIdempotent

Analyze a local .xlsx file to infer column schema: types, nullable flags, header row, and sample values. Enables type-aware data handling decisions before reading or processing.

Instructions

infer column schema of a LOCAL .xlsx file — types, nullable flags, header row, sample values. Use when the agent needs to reason about column types BEFORE deciding how to handle data. Includes confidence (high/medium/low) per column.

USE WHEN: the user references a LOCAL file path and you need to understand column types before processing or writing code against the data. Useful before xlsx_read when downstream handling depends on types.

DO NOT USE WHEN: the file came from an upload/attachment. Or for in-memory data the agent already holds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_b64Yes
optionsNo
Behavior4/5

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

Annotations already specify read-only, idempotent, non-destructive. Description adds the critical constraint of local file requirement and reveals output includes confidence levels. No contradiction with annotations.

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?

Well-structured with purpose sentence, usage hints, and explicit when/not. Front-loaded and mostly concise, though some repetition of 'LOCAL' and the usage section could be tighter.

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?

Covers purpose, usage, and behavioral constraints well. Lacks parameter format details and a more detailed description of the return structure (though output components are listed). Without output schema, description should provide more specifics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage. Description mentions file_b64 (required) and options (range, sheet) but does not clarify that file_b64 is base64-encoded content, nor does it explain range/sheet format. Meaning added is minimal.

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 infers column schema of local .xlsx files, listing output components (types, nullable flags, header row, sample values, confidence). It distinguishes from siblings like xlsx_read by emphasizing its use before reading data and that it only handles local files.

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

Usage Guidelines5/5

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

Explicit 'USE WHEN' and 'DO NOT USE WHEN' sections provide clear guidance: use for local file paths before processing, not for uploads/attachments or in-memory data. Also mentions its utility before xlsx_read.

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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