UNICEF Stats MCP

Name: UNICEF Stats MCP
Author: jpazvd

作者：jpazvd版本 1.5.5i★ –i在你的电脑上运行

注册名称io.github.jpazvd/unicefstats-mcp

安装

概览

作者提供的说明（英文原文）

UNICEF child development statistics: 790+ indicators, 200+ countries, SDMX-sourced. No API key.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i非常新↓ 39/周 · ★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 数据库与数据集

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "unicefstats": {
      "command": "uvx",
      "args": [
        "unicefstats-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add unicefstats -- uvx unicefstats-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "unicefstats": {
      "command": "uvx",
      "args": [
        "unicefstats-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "unicefstats": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "unicefstats-mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 jpazvd 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“数据库与数据集”中的其他服务器比较i

星标数多于此分类中其他 1151 个服务器的 0%
发布到注册中心的时间比此分类中其他 1151 个服务器的 14% 更新
基础检查结果等于或优于此分类中其他 1151 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

UNICEF Stats MCP 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.5.5）于5个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Supabase, MongoDB MCP Server, Elasticsearch MCP Server

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Supabase

MCP server for interacting with the Supabase platform

作者：supabase广泛使用↓ 23.5万/周★ 2993前天更新

电脑或云端✓ supabase.com

安装

MongoDB MCP Server

MongoDB Model Context Protocol Server

作者：mongodb-js广泛使用↓ 15.7万/周★ 11574天前更新

在你的电脑上运行

安装

Elasticsearch MCP Server

MCP server for interacting with Elasticsearch

作者：cr7258正在增长↓ 2711/周★ 31514个月前更新

在你的电脑上运行

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

unicefstats-mcp

Experimental — not an official UNICEF product. Verify retrieved values against the UNICEF Data Warehouse before citing in publications. See Limitations.

MCP server for UNICEF child development statistics. Query 790+ child-focused indicators across 200+ countries with disaggregations by sex, age, wealth quintile, and residence. No API key required.

Indicators cover child mortality, nutrition, education, child protection, WASH (water/sanitation/hygiene), HIV/AIDS, immunization, early childhood development, and more. Many align with SDG targets, but the dataset is broader than SDGs alone.

Data source: UNICEF SDMX API

Identity

Property	Value
MCP identity	`io.github.jpazvd/unicefstats-mcp`
PyPI package	`unicefstats-mcp`
Canonical source	github.com/jpazvd/unicefstats-mcp
Data source	UNICEF Data Warehouse via SDMX REST API
Maintainer	Joao Pedro Azevedo (`jpazvd`)
Status	Experimental — not endorsed by UNICEF

Third-party aggregator listings (LobeHub, Smithery, mcp.so, Glama) are not controlled by the maintainer. Verify against the canonical source above.

How it relates to the unicefdata packages
How it compares to other data MCPs
Landscape: MCP servers for official statistics
Relationship to sdmx-mcp
Quick Start
Tools
Demo
Prompts
Benchmark Results
Deployment
Development
Contributing
Limitations and Hallucination Risks
Provenance and Ownership
How to Verify This MCP
License

Key documents

Document	Description
PROVENANCE.md	Data origin, ownership, distribution pipeline, verification steps
CHANGELOG.md	Version history (v0.1.0–v1.5.5) with sources cited
RELEASE.md	Release process checklist and version management
CONTRIBUTING.md	Development setup, code style, PR guidelines
CODE_OF_CONDUCT.md	Contributor Covenant v2.1
examples/RESULTS.md	Full 300-query benchmark analysis with EQA decomposition
examples/LITERATURE_REVIEW.md	Literature review: MCP servers for official statistics — ecosystem, patterns, evaluation, 15 papers
examples/LANDSCAPE.md	Landscape analysis: 20 servers reviewed in depth (March 2026), 92 catalogued with status labels (October 2026) — timeline, feature matrix, strengths/weaknesses
examples/results/related_work.md	Annotated bibliography — 15 papers on tool-augmented hallucination
examples/results/statistical_summary.md	Wilcoxon, bootstrap CI, McNemar tests on benchmark results
examples/MCP-DIRECTORY-STATS.md	Comprehensive directory of all official statistics MCP servers
examples/mcp-smoke-test/MULTIMODEL_SMOKETEST.md	Cross-model smoke test (Anthropic / OpenAI / Google / OpenRouter) for the v0.7.3 cross-provider generalisation question — design, rubric, ~$1 default run, path to full mini-EQA

How it relates to the unicefdata packages

unicefstats-mcp is not a replacement for the unicefdata packages in Python, R, or Stata. They serve different audiences:

	unicefstats-mcp	unicefdata (Python/R/Stata)
Audience	AI assistants (Claude, Cursor, Copilot)	Data scientists, researchers, analysts
Interface	MCP protocol (tool calls via JSON)	Native language API (`library()`, `import`, `ssc install`)
Use case	Conversational data exploration, quick lookups, AI-assisted analysis	Reproducible research, ETL pipelines, statistical analysis
Output	JSON (compact or full) optimized for LLM context	DataFrames, tibbles, Stata matrices
Scripting	No — single queries via AI chat	Yes — full programmatic control, loops, joins, transforms
Caching	Delegates to unicefdata	Built-in SDMX response caching
Bulk download	Limited (max 500 rows per call)	Unlimited — designed for full dataset pulls

Under the hood, unicefstats-mcp wraps the unicefdata Python package. Every tool call ultimately calls unicefdata.unicefData() or its metadata functions. Think of the MCP as a thin AI-friendly interface on top of the same data layer.

When to use which:

Use unicefstats-mcp when you're chatting with an AI and want to quickly explore indicators, check values, or compare countries
Use unicefdata (Python/R/Stata) when you're writing scripts, building dashboards, running regressions, or doing any reproducible analytical work

How it compares to other data MCPs

Servers are named by repository and carry a status label (official, independent, community or commercial) as defined in Status labels. "—" means the feature is not documented in that repository's public README. See About these tables for how counts are taken and how to propose a correction.

Feature	unicefstats-mcp (this repo)	data360-mcp (World Bank, official)	fred-mcp-server (community)	world_bank_mcp_server (community)
Tools	9 (search → metadata → data → code → identity + strict canonical lookup)	16 (search indicators/datasets → metadata → data → disaggregation → codelist lookup → list; summarize / rank / compare countries; country-group expansion; single- and multi-indicator Vega-Lite viz specs; API-URL; plus an interactive-choice UI tool)	3 (browse → search → get)	1 (get only)
Indicators	790+ child-focused indicators	Data360 catalogue, multiple databases (WB_WDI, WB_SSGD, …)	800,000+ economic series	~1,600 indicators
Countries	200+ (ISO3)	200+ (ISO3 `REF_AREA`)	US-focused (some intl)	200+ (ISO2)
Disaggregations	Sex, age, wealth quintile, residence	Sex, age, urbanisation (per indicator, via `data360_get_disaggregation`)	Frequency, seasonal adjustment	None
MCP Prompts	5 (`compare_indicators`, `trend_analysis`, `country_profile`, `sdg_progress`, `write_unicefdata_code`)	7	None	None
MCP Resources	6 (`system-prompt`, `context`, `llm-instructions`, `categories`, `countries`, `glossary`)	11 (`system-prompt`, `context`, `agent-recipe`, `databases`, `codelists`, `data-schema`, `data-filters`, `metadata-fields`, `search-usage`, `viz/chart-grammar`, …)	None	None
Output modes	Compact (5 cols) / Full (all cols)	SDMX-shaped JSON with a per-observation `claim_id` (8-char "proof-carrying numbers" hash); compact serializer for aggregation tools	JSON	CSV
Data summary	Value range, year range, country count	`summarize_data`, `rank_countries`, `compare_countries` tools; Vega-Lite chart specs	None	None
Pagination metadata	`total_rows_available` vs `rows_returned`	`limit`/`offset` with `total_count`, `has_more`, `next_offset` (MCP best-practice pattern)	`limit`/`offset`	None (hardcoded 20K)
Input validation	ISO3, sex, wealth, residence validated	Codelist resolution tool + server-side country-coverage check (`required_country`)	Zod schemas	None
Error guidance	`error` + `tip` with next steps	Structured `error_code` (e.g. `http_error:search`) + message, raised as MCP `ToolError`	HTTP status text	Raw exception
API key	Not required	Not required	FRED_API_KEY required	Not required
Truncation handling	`rows_truncated` flag + filter tips	Payload stripping to core fields with `claim_id` preserved (`docs/payload_analysis.md`)	None	None
Published	PyPI	PyPI	npm	No

Landscape: MCP servers for official statistics

This project is part of a growing ecosystem of MCP servers for international and official statistics. Tables group servers by the data source they wrap; the Status column says who maintains them, using the labels defined in Status labels.

About these tables

Status labels

Status describes who maintains a server, not its quality or the quality of its data.

Official: published by the organisation that produces the data, or that operates the platform it is served from, under that organisation's account or announced on its own website. A qualifier marks maturity where the organisation states it: *(beta)*, *(lab)*, *(pilot)*.
Independent: built by staff of that organisation in a personal capacity, as stated in the README or the maintainer's profile, and not endorsed by the organisation. This repository is in this category.
Community: built by individuals or groups with no stated affiliation to the data producer.
Commercial: offered by a company as part of a product or a hosted or paid service, with no stated affiliation to the data producer.

*Official* refers to the server, not the data. Servers whose data are not official statistics, such as research estimates, are listed separately in the directory; a research producer that maintains its own server is labelled *producer-maintained*, not official.

When the evidence is ambiguous, we do not claim official or independent status: the row is labelled community and the note says why. Affiliations we cannot see can be added through the landscape-correction issue template below.

Counts and corrections

The comparison and landscape tables are a snapshot, not a registry. Every count (tools, prompts, resources, stars) describes what is visible on each repository's public default branch on the date of the commit that last touched the row — currently 2026-10-24 to 2026-10-27 — and the ecosystem moves faster than this README. The

只显示了开头部分；其余内容请在仓库中阅读