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data-profiler-mcp

data-profiler-mcp

by haiiibin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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column_stats

Compute detailed statistics for one column: numeric metrics like percentiles, outliers, histogram; top values for text; timestamp range for dates. Use when a column needs deeper inspection after profiling.

Instructions

Deep statistical dive on a single column.

For numeric columns: min/max, mean, std, a full set of percentiles (p1/p5/q1/median/q3/p95/p99), skewness, kurtosis, zero and negative counts, an IQR-based outlier count with bounds, and a 10-bin histogram. For datetime columns: the min and max timestamp. For text/categorical columns: the top values with counts and percentages, plus string-length statistics.

Reach for this after profile_dataset when one column needs closer inspection. Raises an error listing the available columns if column is not found.

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>",
      "column": "<column>"
    },
    "name": "column_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
columnYes
max_rowsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden. It thoroughly explains the output for numeric, datetime, and text columns, and discloses error behavior. As a read-only analysis tool, no destructive actions are implied, but it could explicitly state that no modifications are made.

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 concise with two well-structured paragraphs. The first sentence front-loads the purpose, followed by detailed bullet points. Every sentence adds value, and there is no redundancy or wasted words.

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?

Despite lacking an output schema, the description provides a comprehensive overview of the returned statistics for each column type. It also covers error behavior. For a focused single-column analysis tool, this level of detail is complete and sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for parameter meanings. It only mentions the 'column' parameter in error context but fails to explain 'path' and 'max_rows'. This is a significant gap for a tool with 3 parameters.

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's purpose as a 'deep statistical dive on a single column' and details the statistics for different column types. It distinguishes from sibling tool 'profile_dataset' by suggesting usage after it for closer inspection, providing specificity and differentiation.

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?

The description explicitly says to use 'after profile_dataset when one column needs closer inspection' and mentions error behavior when column is not found. While clear context is provided, there is no explicit exclusion of other sibling tools or when-not usage.

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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