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mne_load_raw

Loads raw EEG/MEG/neurophysiology recordings from disk into the current session. Auto-detects file format by extension and supports preload toggle for large files.

Instructions

Load a raw recording from disk into the session. Auto-detects the format by extension (FIF/EDF/BDF/BrainVision/EEGLAB/CNT/EGI/…). Stores it under name (default raw). Set preload=False for very large files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoraw
pathYes
preloadNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It discloses auto-detection of format by extension and the preload behavior for large files, giving some insight into functionality, but it omits potential error cases, unsupported formats, or detailed memory usage consequences beyond the preload hint.

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 three sentences, front-loaded with the primary purpose, followed by format detection and parameter guidance. Every sentence adds value without redundancy, making it appropriately concise and well-structured.

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?

Given the tool's simplicity with only three parameters and the existence of an output schema, the description covers the main use case and key parameters effectively. It lacks some edge-case context (e.g., what happens if a format is unsupported or file missing), but for a straightforward load tool, it is sufficiently complete.

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?

The schema has zero description coverage, so the description compensates by explaining the `name` parameter (storage variable with default) and `preload` parameter (for very large files). The `path` parameter is self-evident from context, and the auto-detection detail adds meaning to the extension handling. No additional details on path validation are given, but core parameters are covered.

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?

Description clearly states the tool's function: loading a raw recording from disk into the session. It uses a specific verb ('Load') and resource ('raw recording'), and differentiates from sibling tools by focusing on data acquisition rather than processing or saving.

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?

Provides clear context for when to use the tool: when needing to load raw data into the session. It includes practical guidance on using preload=False for large files, but does not explicitly contrast with alternative tools or state when not to use it, though no direct alternative exists among siblings.

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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