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koizumikento

e-Stat MCP

by koizumikento
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_stats_data_bulk

Read-only

Fetches statistical data for multiple dataset or table identifiers in one bulk request, streamlining multi-source retrieval from the e-Stat government portal.

Instructions

複数の統計表ID/データセットIDから統計データを一括取得する.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo後方互換用の取得件数
requestsNostatsDatasSpecに入れる取得条件のリスト
dataset_idsNo後方互換用のデータセットIDリスト
start_positionNo後方互換用のデータ取得開始位置
stats_data_idsNo後方互換用の統計表IDリスト

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.4.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / dataset_ids / description
      Added value: +"後方互換用のデータセットIDリスト"
    • addedInput schema / properties / limit / description
      Added value: +"後方互換用の取得件数"
    • addedInput schema / properties / requests
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "statsDatasSpecに入れる取得条件のリスト"
+}
    • addedInput schema / properties / start_position / description
      Added value: +"後方互換用のデータ取得開始位置"
    • addedInput schema / properties / stats_data_ids / description
      Added value: +"後方互換用の統計表IDリスト"
  2. First observed

TDQS

B3.1/5.0
Behavior3/5

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

The description adds no behavioral context beyond the annotations: '取得する' (fetch) is consistent with readOnlyHint=true, so there is no contradiction. The only added behavior is the bulk-across-multiple-IDs nature, which is largely implied by the tool name. It does not warn about potentially large responses from bulk fetches or what happens if one ID in the batch fails.

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?

A single, front-loaded Japanese sentence with zero filler, conveying purpose and scope efficiently. Slightly under-specified rather than verbose, which is acceptable conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite an output schema and readOnly annotation, the tool is a complex bulk operation with a primary free-form `requests` field whose structure is unexplained. The description does not cover request-spec semantics, whether IDs are AND/OR combined, or batch/limit behavior. For a bulk tool that is the heavier counterpart of get_stats_data, this is a meaningful completeness gap.

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 coverage is 100%, so per the rubric the baseline is 3. The description adds only marginal value by mapping the 'multiple IDs' phrasing onto stats_data_ids/dataset_ids. It does not compensate for the opaque `requests` parameter, which is a free-form object (additionalProperties: true) referencing the API-internal 'statsDatasSpec' — the agent cannot infer its structure without outside knowledge.

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 states a specific verb (一括取得する, bulk-fetch), a concrete resource (統計データ, statistical data), and the scope (複数の統計表ID/データセットID, multiple table/dataset IDs). The 'bulk' + 'multiple IDs' framing clearly separates it from the sibling get_stats_data, though it never names the sibling explicitly.

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?

No guidance is given on when to prefer this tool over get_stats_data or the CSV variants. Nothing states 'use this when fetching multiple tables at once', no exclusions, no context about payload-size trade-offs. The definition's own sibling get_stats_data exists and the description never acknowledges the choice.

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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