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Gakuji3

csv-analyzer-mcp

by Gakuji3

get_csv_summary

Get a quick JSON summary of a CSV file with row and column counts, column names, data types, numeric columns, and missing value totals. Provides structural insights for programmatic analysis.

Instructions

CSV ファイルの JSON 形式でのクイックサマリーを取得します。

このツールは CSV ファイルの構造と基本情報の簡潔なサマリーを提供します。 プログラマティックな処理に有用です。

Args: file_path: CSV ファイルへの絶対パスまたは相対パス

Returns: 以下の情報を含む JSON 文字列: - rows: 行数 - columns: 列数 - column_names: 列名のリスト - column_types: 列名とデータ型の辞書 - numeric_columns: 数値列の列名リスト - total_missing_values: 欠損値の数

Example: >>> await get_csv_summary("/Users/username/data.csv") # JSON サマリーを返します

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden of disclosure and does a good job by enumerating the exact return fields (rows, columns, column_names, etc.) and providing an example invocation. It does not cover error handling or file access behavior, but for a read-only summary tool the essential behavioral contract is clear.

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?

The description is well-structured with Args, Returns, and Example sections, and the first sentence immediately conveys the purpose. A few phrases like 'this tool provides' are slightly redundant but not wasteful.

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?

For a single-parameter tool, the definition provides parameter semantics, detailed return values, and an example, giving an agent enough to invoke it correctly. The main gap is not addressing how it relates to analyze_csv, which is a minor context shortfall.

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?

The schema provides no description for file_path (0% coverage), so the description's clarification that it accepts 'absolute or relative path' adds essential meaning. The example also demonstrates realistic usage, though no additional constraints or formats are specified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('get a quick summary' of a CSV file) and the output format (JSON). It identifies the resource precisely, but does not differentiate from the sibling tool analyze_csv, so it falls just short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Beyond the generic note that it is 'useful for programmatic processing', the description gives no guidance on when to use this tool versus analyze_csv or any exclusions. No alternatives or selection criteria are mentioned.

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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