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analyze_file

Analyze large files (CSV, Excel, PDF, JSON) and get a token-efficient summary with schema, statistics, and sample data in ~300-500 tokens.

Instructions

Analyze a large file (CSV, Excel, PDF, JSON) and return a token-efficient summary. Instead of pasting thousands of rows into the chat, use this to get schema, statistics, and sample data in ~300-500 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoWhat you want to know about the file (improves relevance)
file_pathYesAbsolute path to the file to analyze
token_budgetNoMax tokens for the summary (default: 500)
Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses the tool's token efficiency (~300-500 tokens) and what the summary includes (schema, statistics, sample data). No contradiction detected.

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?

Two sentences, front-loaded with purpose and benefit, no redundancy. Every sentence earns its place.

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

Completeness4/5

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

For a tool with 3 parameters and no output schema, the description adequately explains the return format (schema, statistics, sample data) and token efficiency. Minor gap: no mention of error handling or prerequisites.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by explaining the 'query' parameter improves relevance and the 'token_budget' parameter is for max tokens. This goes beyond the schema's bare descriptions.

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 'analyze' with a resource 'large file' and lists supported file types (CSV, Excel, PDF, JSON). The sibling tools are unrelated, so differentiation is clear.

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 explicitly states a use case: 'Instead of pasting thousands of rows into the chat, use this...' It implies when to use but does not explicitly state when not to use or list alternatives.

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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