japan-data-mcp

describe_japan_dataset

Read-onlyIdempotent

Retrieve a Japanese dataset's exact column schema, data types, historical coverage, ministry source, and sample record to verify fields before SQL queries or searches.

Instructions

Retrieve the exact column schema, data types, historical coverage range, primary government ministry source, and a representative sample record (including English 'prefecture_en' and 'city_en') for a single dataset. WHEN TO USE: Call this after 'list_japan_datasets' and before 'execute_japan_megabank_sql' or 'search_japan_official_data' to verify exact column names and data formats. WHEN NOT TO USE: Do not use this to search or filter multiple records (use 'search_japan_official_data' for filtered rows or 'execute_japan_megabank_sql' for aggregations).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dataset_id`	Yes	Exact dataset identifier obtained from 'list_japan_datasets' (lowercase snake_case, e.g., 'demographics', 'procurement', 'medical', 'renewable_energy', 'companies').

Output Schema

TableJSON Schema

Name	Required	Description
`schema`	No	Mapping of column names to their data types and formats.
`dataset_id`	No	The inspected dataset identifier.
`sample_record`	No	A single sanitized sample row demonstrating actual field values.

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv1.0.4Oct 15, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

changedInput schema / properties / dataset_id / description

Previous value: -"Dataset identifier (e.g. 'demographics', 'procurement', 'medical', 'renewable_energy', 'companies')."New value: +"Exact dataset identifier obtained from 'list_japan_datasets' (lowercase snake_case, e.g., 'demographics', 'procurement', 'medical', 'renewable_energy', 'companies')."

addedOutput schema / properties / dataset_id / description
```
Added value: +"The inspected dataset identifier."
```

addedOutput schema / properties / sample_record / description

Added value: +"A single sanitized sample row demonstrating actual field values."

addedOutput schema / properties / schema / description

Added value: +"Mapping of column names to their data types and formats."

First observedv0.1.0Oct 15, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds useful behavioral context about what is returned (column schema, types, historical range, ministry source, sample record including prefecture_en/city_en), but says nothing about auth, rate limits, or failure modes.

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?

Front-loaded purpose followed by clearly labeled WHEN TO USE / WHEN NOT TO USE sections. Every sentence carries routing or content information with no filler.

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 explained, yet the description still previews the payload and fully routes the agent in a 1-param, single-sibling-cluster tool. Nothing needed to call it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100% and the single dataset_id param is fully documented in the schema, including the snake_case format and examples. The description adds only the 'single dataset' scope, so baseline 3 applies.

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?

States a specific verb (Retrieve) and resource (column schema, data types, coverage, sample record for a single dataset). It clearly distinguishes itself from search/filter and aggregation siblings by scoping to one dataset's metadata.

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?

Explicit WHEN TO USE (after list_japan_datasets, before execute/search to verify column names and formats) and WHEN NOT TO USE (not for searching/filtering rows or aggregations) with the exact sibling alternatives named.

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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