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detect_pattern

Identify a dataset's structure by scanning a file or directory, and receive suggested load_source arguments to load it directly.

Instructions

Scan a directory (or single file) and identify its multi-file structure.

    Recognizes: partitioned_parquet, partitioned_csv, mysql_dump, multi_hdf5,
    related_tables, image_folder, text_corpus, single_file. Returns suggested
    load_source(args) the agent can invoke directly.

    Output size: small to medium (file lists capped at 50).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It specifies recognized pattern types, the return format (a suggested load_source call), and an output size cap (file lists capped at 50). It does not mention permissions or failure modes, but for a read-only scanning tool this is adequate and goes beyond a vague description.

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 efficient and front-loaded, leading with the core purpose. The list of patterns and output cap adds valuable detail without unnecessary verbosity. It could be slightly tighter, but 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 one parameter and an output schema, the description covers the input type and output behavior adequately. It mentions output size and actionable return value, and while it could note error handling or read-only guarantees, the combination of description and output schema makes it sufficiently complete.

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

Parameters5/5

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

The schema has a single 'path' parameter with 0% description coverage, so the description must compensate. It does so by explicitly stating that the path can be a directory or a single file, and the list of recognized patterns gives further context about what the parameter should point to.

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 states a specific action ('Scan a directory (or single file) and identify its multi-file structure') and enumerates concrete recognized patterns (e.g., partitioned_parquet, mysql_dump). This clearly distinguishes it from sibling tools like list_files or describe_source, 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 implies a clear use case: run this tool to understand a dataset's multi-file structure before calling load_source. It even provides the workflow by returning suggested load_source(args). However, it does not explicitly name alternative tools or state when not to use it, so it falls short of full differentiation.

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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