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Anthropic Economic Index MCP Server

@Anthropic Economic Index

基本情報

トランスポート

streamable-http

認証方式

提供元の文書を確認

カテゴリ

データと分析

作者

Anthropic Economic Index

Anthropic Economic Index MCP Server について

Anthropic が Anthropic Economic Index 向けに提供する公式 MCP サーバーです。この指数は、人々が Claude をどう使っているかを示すオープンな研究データセットです。アシスタントは、Claude の利用状況で国や米国の州をランク付けし、職業や業務タスク別に利用を分析し、地域を比較し、方法論の注記を読むことができます。データは CC BY 4.0 で、サーバーは無料・サインイン不要です。

接続情報

https://econ-index.mcp.claude.com/mcp

セットアップ

claude mcp add anthropic-economic-index --transport http https://econ-index.mcp.claude.com/mcp

Anthropic Economic Index MCP Server のツール

9

Anthropic Economic Index overview: coverage, releases, definitions. Call this once at the start of an exploration. Returns the latest data period and snapshot timestamp, dataset metadata (license, publisher, landing page), temporal_coverage — the published period span; the served numbers are a snapshot of the latest period, not a trend — geography and occupation coverage counts, the worldwide headline split, and a definitions glossary to reuse when presenting numbers to non-experts. country_count counts only countries with a published Anthropic Usage Index, the same cohort econ_index_list_countries / econ_index_get_usage_by_country cover. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

One country's subregions ranked by Claude usage. Defaults to the USA: all 50 states plus Washington, D.C., ranked by the Anthropic Usage Index. Other countries' subregions have no published index — rows rank by usage_share_pct, each subregion's share of the country's observed usage; that is volume, not engagement relative to population size, and map colors should say so. Each row carries the use-case splits, top-5 job categories, top artifact types, and its top-5 request topics with shares — enough for a choropleth with tooltips and a click-through panel from this single call. For one subregion's top-10 categories and full hierarchies, use econ_index_get_usage_by_subregion. For US states, join a us-atlas states topojson by state name (Washington, D.C. is 'District of Columbia' there) and color by anthropic_usage_index; elsewhere search for a suitable topojson when none is at hand and color by usage_share_pct. To compare subregions, compare profiles (each one's categories and topics vs the national average: baseline_pct, vs_baseline_index), not just usage volume. When a visualization tool is available, always visualize: this data is geographic, so lead with a choropleth of a numeric field — the usage index or a split share; a map of each region's top topic or category comes out near-uniform, because the same entries lead nearly everywhere — when no map source for these regions is bundled or known, search for one before any non-map fallback; more than one visualization is fine. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Full Claude-usage profile for one subregion (state, province, prefecture). Covers US states and, where published, other countries' subregions (BR-SP, JP-13, ...). Returns the usage rank within the country, the augmentation/automation and work/personal/coursework splits, top-10 job categories, most frequent request topics, work tasks, and artifact types. The key shares carry the country average alongside (baseline_pct, vs_baseline_index), and the baseline block carries the country's headline figures for side-by-side display. US states also carry the Anthropic Usage Index; other subregions have no published index — present their standing by rank and share, never a made-up index. Pass include_tasks / include_requests for the full occupation-task and request-topic hierarchies (each node carries its 0-100 share of the subregion's usage). Empty lists mean not published — never invent entries. Open with the headline numbers against the country average, then visualize the job categories, then the most frequent topics, then artifact types. When a visualization tool is available, always explore visualizations: consider the data's shape — how many series, whether values carry a baseline comparison, whether anything is geographic — try the available options and judge which fits best; more than one visualization is fine. Geographic data gets a choropleth by default. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Every country with a published Anthropic Usage Index, ranked by it. Each row carries the index and rank, the augmentation/automation and work/personal/coursework splits, the top-5 job categories, top artifact types, and its top-5 request topics with shares — enough for a choropleth with tooltips and a click-through panel from this single call. iso_numeric matches the feature ids of a world topojson (world-atlas countries-110m): join the map on codes, not country names. For one country's top-10 categories and full hierarchies, use econ_index_get_usage_by_country. Lead with a world choropleth colored by anthropic_usage_index; give tooltips the index plus the top job categories or the use-case split. Follow with the top job categories — a country ranking complements the map rather than replacing it. To compare countries, compare profiles (each one's categories and topics vs the global average: baseline_pct, vs_baseline_index), not just usage volume. When a visualization tool is available, always visualize: this data is geographic, so lead with a choropleth of a numeric field — the usage index or a split share; a map of each region's top topic or category comes out near-uniform, because the same entries lead nearly everywhere — when no map source for these regions is bundled or known, search for one before any non-map fallback; more than one visualization is fine. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Full Claude-usage profile for one country. Returns the usage index and rank, the augmentation/automation and work/personal/coursework splits, top-10 job categories, most frequent request topics, work tasks, and artifact types. The key shares carry the global average alongside (baseline_pct, vs_baseline_index), and the baseline block carries the global headline figures for side-by-side display. Pass include_tasks / include_requests for the full occupation-task and request-topic hierarchies (each node carries its 0-100 share of the country's usage). Only countries with published usage are covered. Open with the headline numbers against the global average. For any single-country question, follow up with econ_index_list_subregions for the within-country picture — coverage varies and an empty result says so; where rows exist, lead with the subregion choropleth. Then visualize the job categories, then a brief mention of topics, then artifact types. When a visualization tool is available, always explore visualizations: consider the data's shape — how many series, whether values carry a baseline comparison, whether anything is geographic — try the available options and judge which fits best; more than one visualization is fine. Geographic data gets a choropleth by default. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Occupations and job categories ranked by Claude usage of their tasks. A bare call returns category_shares — every job category with its share of global usage. Lead with those ~22 categories — one ranked series of shares — and visualize them, narrating only the top few. Pass query to drill down: a category name lists its occupations, an occupation name returns that occupation, and any other word matches occupation names. When no slice exists for the exact profession asked, quote the closest published figure with its scope named ('software developers', 'Legal as a whole') — never present broader numbers as the requested slice. Tasks in the gap between task_count and published_task_count are not published; their values may be zero. Global scope only — for one state or country use econ_index_get_usage_by_subregion / econ_index_get_usage_by_country. Answers questions like 'which jobs and careers use AI the most?'. Frame results as "AI is used for tasks commonly done by [occupation]", never "[occupation]s are using AI" — the people in these conversations are often not members of that occupation. When a visualization tool is available, always explore visualizations: consider the data's shape — how many series, whether values carry a baseline comparison, whether anything is geographic — try the available options and judge which fits best; more than one visualization is fine. Geographic data gets a choropleth by default. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Side-by-side headline comparison of 2-8 regions from one cohort. Returns each region's usage index, augmentation/automation split, work/personal/coursework split, top job categories, most common request topics, and top work tasks, plus the shared baseline (the world for countries; the country for its subregions). Regions publish different work-task subsets — compare tasks by name, not rank position. Compare countries with countries, or subregions of one country — indexes are normalized within one cohort and never compare across cohorts, and subregion usage shares are likewise calculated within the parent country. For region profiles beyond the headline, use the detail tools; for a full ranking, the list tools. When a visualization tool is available, always build comparative visualizations — show each region relative to the others and the shared baseline: heatmaps, diverging bar charts, bubble charts, and grouped or stacked bars all fit side-by-side data; go beyond a single bar chart or choropleth, and more than one visualization is fine. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Top work tasks worldwide: what people use Claude for the most, by task. Returns the top 50 work tasks, ranked by their 0-100 share of global sampled conversations. Task descriptions come from O*NET, the US government's catalog of each occupation's tasks, in plain English, and are matched from conversation content. For the breakdown within one occupation, use econ_index_get_occupation_usage. Answers questions like 'what do people in different careers actually use Claude for?'. Task wording is specific enough that questions about one kind of request — financial advice, resume help, lesson planning — often match tasks here directly. When a visualization tool is available, always explore visualizations: consider the data's shape — how many series, whether values carry a baseline comparison, whether anything is geographic — try the available options and judge which fits best; more than one visualization is fine. Geographic data gets a choropleth by default. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

Global Claude usage, occupational task breakdowns, and common request topics. How people use Claude overall including occupational task breakdowns and common request topics — the Anthropic Economic Index worldwide profile. Returns the augmentation/automation and work/personal/coursework splits, the classifier-estimated task-time comparison (hours working alone vs minutes with Claude — normalize the units before charting), top job categories, top request topics, and artifact types. Topic-share questions — how often people ask about money, health, investing, travel, and the like — are answered by top_request_topics, and the worldwide work-task ranking (econ_index_list_top_work_tasks) carries the task-level detail behind the same questions. Whenever the question warrants a country breakdown, call econ_index_list_countries and lead with the world choropleth (its rows carry iso_numeric for a code join), with this profile's splits as compact metric badges above the map; then visualize the job categories. When a visualization tool is available, always explore visualizations: consider the data's shape — how many series, whether values carry a baseline comparison, whether anything is geographic — try the available options and judge which fits best; more than one visualization is fine. Geographic data gets a choropleth by default. Figures are derived from anonymized, aggregated conversation content and cannot be tied to individual users. They describe observed Claude usage by task and are not evidence about employment or job automation.

概要

Anthropic Economic Index MCP Server とは?

Anthropic Economic Index MCP Server は、Anthropic 公式の無料エンドポイント(https://econ-index.mcp.claude.com/mcp)です。世界中の人々が Claude をどう使っているかを示す Anthropic の公開データ、Anthropic Economic Index を、AI アシスタントが国、米国の州、職業、タスク別に探索できるようにします。

Anthropic Economic Index は、AI が経済に与える影響を理解するための研究イニシアチブです。集計した Claude の会話を職務タスクと対応付け、その結果をレポートとオープンデータセットとして CC BY 4.0 ライセンスで公開しています。MCP サーバーはこのデータセットに対話型のインターフェースを与えるもので、研究者、ジャーナリスト、政策アナリスト、関心のあるユーザーが「AI を最も使っている職業は?」と尋ね、出典から直接数値を得られます。Anthropic は Claude のコネクタディレクトリで、これを Anthropic 製のコネクタとして掲載しています。

Anthropic Economic Index MCP Server の使い方

サーバー自体にはアカウント、API キー、サインインのいずれも不要です。URL で追加します。

https://econ-index.mcp.claude.com/mcp

Claude Code

claude mcp add --transport http economic-index https://econ-index.mcp.claude.com/mcp

Claude Desktop と claude.ai

このコネクタは Claude のディレクトリに「Anthropic Economic Index」として掲載されています。claude.ai の設定でコネクタメニューを開き、ディレクトリで見つけて有効にすると、以降はどの会話でも使えます。上記の URL を使ってカスタムコネクタとして追加することもできます。

Cursor

{
  "mcpServers": {
    "economic-index": {
      "url": "https://econ-index.mcp.claude.com/mcp"
    }
  }
}

VS Code

{
  "servers": {
    "economic-index": {
      "type": "http",
      "url": "https://econ-index.mcp.claude.com/mcp"
    }
  }
}

Anthropic Economic Index MCP Server の主な機能

  • Anthropic が公開する公式サーバーで、データは CC BY 4.0 ライセンス。
  • 無料で公開:認証不要。
  • 地理的なカバー範囲:執筆時点で 120 以上の国と、米国の全 50 州およびワシントン D.C.。
  • 職業とタスクのデータ:約 22 の職業カテゴリーと数百の公開済み職業。タスクの記述は O*NET に基づく。
  • 拡張(augmentation)と自動化(automation)、仕事・個人・学業といった利用の内訳。
  • 方法論の注記と定義を内蔵しており、アシスタントが適切な注意書きとともに数値を示せる。
  • 世界地図の作成に使える ISO 国コードなど、可視化を想定したツール出力。

Anthropic Economic Index MCP Server のツール

サーバーは 9 つのツールを提供しており、すべて econ_index_ で始まります。

  • まずはここから:econ_index_get_dataset_overview はカバー範囲、最新のデータ期間、ライセンス、定義、世界全体の内訳を返します。探索の最初に一度呼び出すことを想定しています。
  • 世界全体:econ_index_get_global_usage は世界全体のプロファイルを、econ_index_list_top_work_tasks は Claude が最も使われている上位 50 の業務タスクを返します。
  • 国:econ_index_list_countries は Anthropic Usage Index が公開されているすべての国をランク付けし、econ_index_get_usage_by_country は 1 か国の詳細なプロファイルを返します。
  • 地域:econ_index_list_subregions は国内の地域(既定は米国の州)をランク付けし、econ_index_get_usage_by_subregion は 1 つの州、省、都道府県のプロファイルを返します。
  • 職業と比較:econ_index_get_occupation_usage は職業カテゴリーをランク付けして個々の職業まで掘り下げ、econ_index_compare_regions は同じグループの 2〜8 の地域を並べて比較します。

Anthropic Economic Index MCP Server のユースケース

  • 職業の研究:「AI を最も使っている職業はどれで、どんなタスクに使っている?」
  • 地域の分析:「コロラド州の人は Claude を主に何に使っている?」
  • 国の比較:「日本、ドイツ、ブラジルの Claude の利用状況を比較して」
  • 報道:AI の普及に関する主張を Anthropic の公開数値と照合し、出典を示す。
  • 教育や政策の仕事:実際の利用データを使って、拡張と自動化の違いを説明する。

Anthropic Economic Index MCP Server のトラブルシューティング

  • 数値がトレンドに見えるが、そうではない:サーバーが返すのは最新データ期間のスナップショットです。時系列データはないため、割合の増減は示せません。
  • 指数の値がかみ合わない:Anthropic Usage Index は同じグループの中で正規化されています。国は国同士、地域は同じ国の地域同士で比較し、グループをまたいで比較しないでください。
  • 値が欠けている:null はそのセルの値が公開されていないことを意味し、必ずしもゼロではありません。
  • 目的の職業が見つからない:データセットは利用データが公開された職業のみを扱い、分類体系全体ではありません。最も近い公開済みの職業やカテゴリーを使ってください。

よくある質問

1

Anthropic Economic Index MCP Server は公式ですか?

はい。Anthropic が開発して Claude のコネクタディレクトリに掲載しており、claude.com のドメインから提供されています。

2

無料ですか?

はい。サーバーは認証不要で、基になる Economic Index のデータは CC BY 4.0 ライセンスで公開されています。

3

Anthropic Economic Index は何を測定していますか?

会話を職務タスクと対応付けて観測した Claude の利用状況です。Anthropic は、これは Claude の利用パターンを反映したもので労働市場全体ではなく、このデータだけでは雇用の代替について結論を出せないとしています。

4

どのクライアントで使えますか?

ディレクトリには Claude、Claude API、Claude Code が挙げられています。標準的な Streamable HTTP サーバーなので、Cursor や VS Code などの MCP クライアントでも URL で追加できます。

5

データはどのくらい新しいですか?

執筆時点で、サーバーが示す最新のデータ期間は 2026 年 5 月でした。現在の期間は `econ_index_get_dataset_overview` で確認できます。

6

データの出典は?

サーバーは数値と方法論の出典として anthropic.com/economic-index を挙げており、アシスタントにそのリンクをユーザーへ示すよう求めています。

7

Anthropic Economic Index のリモート MCP サーバーとは何ですか?

Anthropic Economic Index のリモート MCP サーバーは、https://econ-index.mcp.claude.com/mcp にホストされた Model Context Protocol エンドポイントです。AI アシスタントはローカルに何もインストール・実行することなく接続できます。

8

Anthropic Economic Index の MCP サーバーにはどう接続しますか?

エンドポイント https://econ-index.mcp.claude.com/mcp を Claude Code、Cursor、VS Code など任意の MCP 対応クライアントに追加してください。このページのセットアップ手順で各クライアントを一度に設定できます。

9

Anthropic Economic Index の MCP サーバーは認証が必要ですか?

この掲載では Anthropic Economic Index の認証要件がまだ確認されていません。接続前に提供元のドキュメントをご確認ください。

10

Anthropic Economic Index の MCP サーバーはどのトランスポートを使用しますか?

Anthropic Economic Index は streamable-http エンドポイントを公開しており、これはリモート MCP サーバーで広く使われ、主要な MCP クライアントがサポートするトランスポートです。

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