Anthropic Economic Index MCP Server 是官方的吗?
是的。它由 Anthropic 构建并列在 Claude 连接器目录中,通过 claude.com 域名提供服务。
@Anthropic Economic Index
基本信息
这是 Anthropic 为 Anthropic Economic Index 推出的官方 MCP 服务器,该指数是关于人们如何使用 Claude 的开放研究数据集。助手可以按 Claude 使用情况对国家和美国各州排名,按职业和工作任务细分使用情况,对比不同地区,并阅读方法说明。数据采用 CC BY 4.0 许可,服务器免费且无需登录。
https://econ-index.mcp.claude.com/mcpclaude mcp add anthropic-economic-index --transport http https://econ-index.mcp.claude.com/mcpAnthropic 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.
Anthropic Economic Index MCP Server 是 Anthropic 官方的免费端点(https://econ-index.mcp.claude.com/mcp),让 AI 助手可以按国家、美国州、职业和任务探索 Anthropic Economic Index,即 Anthropic 关于全球用户如何使用 Claude 的公开数据。
Anthropic Economic Index 是一项研究 AI 对经济影响的研究计划。它把汇总后的 Claude 对话与工作任务进行匹配,并以 CC BY 4.0 许可将结果作为报告和开放数据集发布。MCP 服务器为这个数据集提供对话式接口,让研究人员、记者、政策分析师和感兴趣的用户可以直接问「哪些职业使用 AI 最多?」,并获得来自源头的数字。Anthropic 在 Claude 连接器目录中将其列为 Anthropic 自建的连接器。
服务器本身不需要账户、API 密钥或登录。通过 URL 添加即可:
https://econ-index.mcp.claude.com/mcp
claude mcp add --transport http economic-index https://econ-index.mcp.claude.com/mcp
该连接器在 Claude 目录中名为「Anthropic Economic Index」。在 claude.ai 设置中打开连接器菜单,在目录里找到并启用它;之后它可以在任何对话中使用。你也可以用上面的 URL 把它添加为自定义连接器。
{
"mcpServers": {
"economic-index": {
"url": "https://econ-index.mcp.claude.com/mcp"
}
}
}
{
"servers": {
"economic-index": {
"type": "http",
"url": "https://econ-index.mcp.claude.com/mcp"
}
}
}
服务器提供九个工具,均以 econ_index_ 为前缀:
econ_index_get_dataset_overview 返回覆盖范围、最新数据周期、许可、定义和全球总体拆分,建议在探索开始时调用一次。econ_index_get_global_usage 返回全球概况,econ_index_list_top_work_tasks 返回人们使用 Claude 最多的前 50 项工作任务。econ_index_list_countries 对所有已发布 Anthropic Usage Index 的国家排名,econ_index_get_usage_by_country 返回单个国家的完整概况。econ_index_list_subregions 对一个国家的次级地区排名(默认是美国各州),econ_index_get_usage_by_subregion 返回单个州、省或都道府县的概况。econ_index_get_occupation_usage 对职业类别排名并可深入到具体职业,econ_index_compare_regions 对同一组别中的 2 到 8 个地区进行对比。是的。它由 Anthropic 构建并列在 Claude 连接器目录中,通过 claude.com 域名提供服务。
免费。服务器无需认证,其背后的 Economic Index 数据以 CC BY 4.0 许可发布。
观察到的 Claude 使用情况,即把对话与工作任务进行匹配。Anthropic 指出,它反映的是 Claude 的使用模式,而不是整个劳动力市场,仅凭这些数据无法得出关于岗位替代的结论。
目录中列出了 Claude、Claude API 和 Claude Code。由于它是标准的 Streamable HTTP 服务器,也可以在 Cursor、VS Code 等 MCP 客户端中通过 URL 添加。
截至撰写时,服务器报告的最新数据周期为 2026 年 5 月。调用 `econ_index_get_dataset_overview` 可以查看当前周期。
服务器把 anthropic.com/economic-index 列为其数字和方法说明的来源,并要求助手把该链接提供给用户。
Anthropic Economic Index 的远程 MCP 服务器是一个托管的 Model Context Protocol 端点,地址是 https://econ-index.mcp.claude.com/mcp,AI 助手无需在本地安装或运行任何东西即可连接。
把端点 https://econ-index.mcp.claude.com/mcp 添加到任意支持 MCP 的客户端,比如 Claude Code、Cursor 或 VS Code。本页的接入代码块可以一步配置好每个客户端。
此收录尚未确认 Anthropic Economic Index 的认证要求。连接前请查阅提供方文档。
Anthropic Economic Index 暴露的是 streamable-http 端点,这是远程 MCP 服务器普遍使用、主流客户端都支持的传输方式。
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