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Inspect Data File

data.inspect_file
Read-onlyIdempotent

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 data.upload 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 with an error that explains how to proceed (upload the file first or switch to a local backend).

CSV/TSV/TXT files have no embedded sampling rate: pass sampling_rate (Hz) for them — the backend never assumes 250 Hz. Formats that embed their rate (EDF/BDF/MAT/H5) ignore it.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "data.inspect_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesABSOLUTE filesystem path (`~` is expanded; local backends only) or the RELATIVE upload path returned by data.upload (works on any backend).
sampling_rateNoHz for CSV/TSV/TXT files (REQUIRED there, e.g. 250.0).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / sampling_rate
      Added value: +{
      +  "anyOf": [
      +    {
      +      "type": "number"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "description": "Hz for CSV/TSV/TXT files (REQUIRED there, e.g. 250.0)."
      +}
  2. Changed1 schema field changed
    • changedInput schema / properties / path / description
      Previous value: -"ABSOLUTE filesystem path (local backends only) or the RELATIVE\nupload path returned by data.upload (works on any backend)."New value: +"ABSOLUTE filesystem path (`~` is expanded; local backends\nonly) or the RELATIVE upload path returned by data.upload\n(works on any backend)."
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, yet the description adds substantial non-obvious behavior: absolute paths are only legal on a local backend and are refused with an explanatory error on hosted backends, uploaded relative paths work everywhere, and the loader assumes volts so µV-native CSVs read 1e6x too large with unitsScale as the remedy. These are exactly the runtime traits an agent cannot infer from annotations.

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?

Four short paragraphs, front-loaded with the summary and then the routing rule, with the caveats grouped last. Dense but nearly every sentence carries operational information; the unit-scaling aside is the only mild tangent, though it is useful.

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 no explanation, and the description nonetheless flags the summary's contents. Backend eligibility, required-parameter conditions, unit pitfalls, and failure behavior are all covered — nothing an agent needs to call this correctly is missing.

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%, so the baseline would be 3, but the description adds real semantics: the path shape itself routes the source (absolute = local disk read, relative upload path = any backend), and sampling_rate is mandatory for CSV/TSV/TXT while being ignored for formats that embed their rate. It could still have stated nothing about `~` expansion, but the schema covers that.

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 first sentence states a specific verb (exploratory summary) and resource (an EEG file), then enumerates exactly what is reported: channels, sampling rate, trial/class balance, per-channel µV stats, band powers, PSD. Supported extensions are listed, so an agent can tell instantly whether its file qualifies.

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?

Explicit when-to-use guidance is given ('Look at the data BEFORE building pipelines') and tied to concrete decisions (class balance drives stratification, flatlined channels mean a montage problem). It also names the fallback when an absolute path fails (upload first or switch backends). It does not explicitly contrast with data.inspect_dataset, which is the nearest sibling.

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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