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data-profiler-mcp

data-profiler-mcp

by haiiibin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

preview_data

Inspect actual rows from tabular data files (CSV, Parquet, Excel, JSON) using head, tail, or random sample modes to verify formatting, encodings, and column values before analysis.

Instructions

Peek at actual rows of a data file.

Returns n rows (capped at 100) as records. mode selects which rows: head (default), tail, or sample (random). Use this to see real example values rather than just statistics, for example to check formatting, encodings, or how a specific column looks in practice.

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": "preview_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
nNo
modeNohead
pathYes

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 takes on full burden, disclosing n capped at 100, mode options (head/tail/sample), and purpose. No side effects mentioned but likely none; adequate transparency.

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?

Two concise, front-loaded sentences with no fluff; every word adds value.

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 3 parameters, no output schema, the description covers usage, parameters, and context adequately. Could mention return format explicitly but already covered.

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% but description explains n (number of rows, capped at 100) and mode (with defaults and options), adding meaning beyond raw schema types. Path is implicit but clear.

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 peeks at actual rows of a data file, distinguishing it from siblings like column_stats or profile_dataset by emphasizing real values instead of statistics.

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 explains when to use (to see real example values for formatting, encodings, etc.) but does not explicitly state when not to use or list alternatives, though context is clear.

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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