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Mne Load Raw

mne_load_raw

Load a raw EEG/MEG recording from disk, auto-detecting its format (FIF, EDF, BDF, BrainVision, etc.) for analysis. Optionally skip preloading 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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description discloses key behaviors: it stores the object under a name, handles format auto-detection, and notes a memory optimization (preload=False for large files). It does not mention return values or side effects, but for a loading tool, this is adequate.

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 efficient sentences, front-loading the core action and then providing key usage nuances. No wasted words.

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, a loading tool with clear parameters, the description covers essential usage. The output schema exists, so return values need not be described. Minor gap: doesn't mention error handling or supported formats explicitly, but auto-detection implies coverage.

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 0%, so the description must compensate. It explains the `name` parameter (default 'raw') and `preload` (false for large files), but `path` is only implicitly covered. It adds value beyond the bare schema by explaining when to set preload.

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 loads a raw recording from disk and specifies the storage name and preload option. It distinguishes itself from siblings by focusing on the loading step, which is unique among the listed tools.

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?

It mentions auto-detection of format by extension and hints at usage for large files with preload=False. It does not explicitly mention when to avoid this tool versus others, but the context implies it's the initial step before processing, which is reasonably clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.