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Inspect File

inspect_file

Summarize an EEG file to reveal channels, sampling rate, class balance, signal stats, band powers, and PSD before BCI pipeline design.

Instructions

Exploratory summary of an EEG file (.edf/.bdf/.mat/.csv/.tsv/.txt/.h5): channels, sampling rate, trial/class balance, per-channel µV stats, band powers and a PSD overview.

The path shape picks the source: ABSOLUTE path → read the file from disk (only on a LOCAL backend: desktop app / MCP local mode — no upload needed); RELATIVE path (the one upload_data returns) → describe the uploaded file, which works on ANY backend (hosted or local).

Look at the data BEFORE building pipelines: class balance drives stratification choices, and flatlined channels mean a montage/reference problem worth fixing first. Units note: values are ASSUMED volts by the loader — a µV-native CSV reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed. On a hosted backend absolute paths are refused and this returns guidance (upload the file first or switch to a local backend).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses backend-specific path rules, that hosted backends refuse absolute paths and return guidance, that disk reads work only on local backends, and that values are assumed volts with a 1e6 scaling hazard for µV-native CSVs. This is exactly the kind of operational context the schema cannot convey.

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?

Front-loads the one-line summary and then layers guidance in a logical order. It is somewhat long and the closing sentence about hosted backends restates the earlier local-backend constraint, adding a little redundancy without new meaning.

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

Completeness5/5

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

An output schema exists, so return values need not be re-explained, yet the description already frames what the summary contains. Combined with backend, upload, and unit-scaling caveats, an agent has everything needed to call this correctly on either backend.

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 0% description coverage for the single `path` parameter, so the description must compensate, and it does: absolute paths are read from disk on local backends while relative paths (from upload_data) resolve the uploaded file on any backend. That is precise semantic guidance well beyond the bare string type.

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?

Opens with a specific verb and resource ('Exploratory summary of an EEG file') and enumerates the exact contents returned (channels, sampling rate, class balance, per-channel µV stats, band powers, PSD overview) plus the supported formats. The resource is precisely delimited to a single file, which keeps it distinct from the dataset-oriented siblings despite no explicit naming.

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?

Explicitly tells the agent when to run it ('Look at the data BEFORE building pipelines') and why each output matters, and it spells out the absolute-vs-relative path decision and which backends each branch works on. Backend refusal conditions are stated so the agent knows the failure mode in advance.

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