Server data from the Official MCP Registry
UNICEF Data (UN Children's Fund) MCP — global statistics on child health,
About
UNICEF Data (UN Children's Fund) MCP — global statistics on child health,
Remote endpoints: streamable-http: https://gateway.pipeworx.io/unicef/mcp
Security Report
Valid MCP server (1 strong, 0 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (595/596 approved). 1 finding(s) downgraded by scanner intelligence.
22 tools verified · Open access · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-pipeworx-io-unicef": {
"url": "https://gateway.pipeworx.io/unicef/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
mcp-unicef
UNICEF Data (UN Children's Fund) MCP — global statistics on child health,
Part of Pipeworx — an MCP gateway connecting AI agents to 1476+ live data sources.
Tools
| Tool | Description |
|---|---|
list_dataflows | Browse or keyword-search UNICEF's datasets (dataflows). UNICEF Data covers child health, nutrition, education, child protection, child mortality, child poverty, immunization, water/sanitation/hygiene (WASH) and the child-related SDGs. Each result has an id (the dataflowId you pass to dataflow_structure / get_data), a version, and an English name. There are ~70 datasets; pass query to filter. Example: list_dataflows({ query: "mortality" }) or list_dataflows({ query: "nutrition" }). |
dataflow_structure | Get the structure (Data Structure Definition) of one UNICEF dataset: its ordered dimensions and, for each, the valid codes (e.g. countries, indicators, sex, age, wealth quintile). Use this to learn how to build the dot-separated SDMX key for get_data. The key has one position per dimension, in dimension_order; an empty position is a wildcard. Always call this before get_data. Example: dataflow_structure({ dataflow_id: "CME" }). |
get_data | Pull observations from a UNICEF dataset. key is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Call dataflow_structure first to learn the dimension order and valid codes. Example: get_data({ dataflow_id: "CME", key: ".CME_MRY0T4.." }) filters by INDICATOR=CME_MRY0T4 (under-five mortality rate) and wildcards REF_AREA/SEX/WEALTH_QUINTILE. Omit key (or pass "") to fetch all series — can be large. Returns decoded series with their dimension labels and per-period values. |
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"unicef": {
"url": "https://gateway.pipeworx.io/unicef/mcp"
}
}
}
What this endpoint actually serves
tools/list at https://gateway.pipeworx.io/unicef/mcp returns the tools in the table
above plus the shared Pipeworx meta-tools — ask_pipeworx,
discover_tools, search_within, remember/recall and the rest of the
gateway-wide set. So the tool count you see is larger than this table: a
single-pack endpoint currently lists roughly 30 shared tools alongside the
pack's own. The connection's initialize response states its exact scope, and
is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a
scoped connection answer a question this pack does not cover — via
ask_pipeworx, which routes across the whole catalog — without you adding a
second MCP server. There is currently no way to mount a pack endpoint without
them; if the extra schemas cost you more context than the routing is worth,
connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}
Both URLs reach the same gateway and the same 1476+ data sources. The
only difference is which pack's tools are listed directly; ask_pipeworx
reaches all of them from either one.
Using with ask_pipeworx
Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:
ask_pipeworx({ question: "your question about Unicef data" })
The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
Reviews
No reviews yet
Be the first to review this server!
More Developer Tools MCP Servers
Git
Freeby Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
Fetch
Freeby Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
Toleno
Freeby Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
mcp-creator-python
Freeby mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.
MCP Marketplace
Freeby mcp-marketplace · Developer Tools
Search and install MCP servers from inside your AI client.
MarkItDown
Freeby Microsoft · Content & Media
Convert files (PDF, Word, Excel, images, audio) to Markdown for LLM consumption