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.

Contents

• 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–v0.4.0) 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	20 official statistics MCP servers compared — 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

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

Feature	unicefstats-mcp	FRED MCP	World Bank MCP
Tools	8 (search → metadata → data → code → identity)	3 (browse → search → get)	1 (get only)
Indicators	790+ child-focused indicators	800,000+ economic series	~1,600 indicators
Countries	200+ (ISO3)	US-focused (some intl)	200+ (ISO2)
Disaggregations	Sex, age, wealth quintile, residence	Frequency, seasonal adjustment	None
MCP Prompt	compare_indicators	None	None
Output modes	Compact (5 cols) / Full (all cols)	JSON	CSV
Data summary	Value range, year range, country count	None	None
Pagination metadata	total_rows_available vs rows_returned	limit/offset	None (hardcoded 20K)
Input validation	ISO3, sex, wealth, residence validated	Zod schemas	None
Error guidance	error + tip with next steps	HTTP status text	Raw exception
API key	Not required	FRED_API_KEY required	Not required
Truncation handling	rows_truncated flag + filter tips	None	None

Landscape: MCP servers for official statistics

This project is part of a growing ecosystem of MCP servers for international and official statistics. As of March 2026:

UN Agencies

Server	Data Source	Tools	SDMX	Published
unicefstats-mcp (this repo)	UNICEF Data Warehouse	7	Yes	PyPI
sdmx-mcp	Any SDMX registry	23	Yes	No
unicef-datawarehouse-mcp	UNICEF Data Warehouse	3	Yes	No
mcp_unhcr	UNHCR refugee data	5	No	No
medical-mcp	WHO GHO / FDA / PubMed	18	No	npm

International Organizations

Server	Data Source	Tools	SDMX	Published
fred-mcp-server	FRED (800K+ series)	3	No	npm
world_bank_mcp_server	World Bank Open Data	1	No	No
imf-data-mcp	IMF (IFS, BOP, WEO)	10	Yes	PyPI
OECD-MCP	OECD (5,000+ datasets)	9	Yes	npm
eurostat-mcp	Eurostat EU statistics	7	Yes	No

National Statistics Offices

Server	Data Source	Tools	Published
us-census-bureau-data-api-mcp	US Census Bureau (official)	5	No
us-gov-open-data-mcp	40+ US Gov APIs	300+	npm
ibge-br-mcp	Brazil IBGE (227 tests)	22	npm
ukrainian-stats-mcp-server	Ukraine SDMX v3	8	npm
istat_mcp_server	Italy ISTAT SDMX	7	No

Known gaps

No MCP server exists for: FAO/FAOSTAT, UNESCO/UIS (4,000+ education indicators), ILO/ILOSTAT, UNSD SDG API, UN DESA Population, UNDP/HDI.

Full directory with install commands: MCP-DIRECTORY-STATS.md

Relationship to sdmx-mcp

UNICEF also maintains sdmx-mcp, a generic SDMX protocol MCP server. The two servers are complementary, not competing:

	unicefstats-mcp (this repo)	sdmx-mcp
Scope	UNICEF child development data only	Any SDMX registry (UNICEF, Eurostat, OECD, ...)
Tools	7 (analyst-friendly, 4-step workflow)	23 (SDMX power-user, structural queries)
Data layer	Wraps unicefdata Python package	Direct SDMX REST API calls via httpx
Output	Formatted for LLMs (compact tables, summaries, tips)	Raw SDMX-JSON/CSV
Accuracy (EQA)	0.990	0.074
Hallucination	7% T1 / 34% T2	0% T1 / 0% T2
Cost per query	$0.018	$0.087
Latency	9.8s avg	60s avg

Key tradeoff: unicefstats-mcp is dramatically more accurate (EQA 0.990 vs 0.074) because its formatted output is optimized for LLM parsing. sdmx-mcp has zero hallucination because its assistant_guidance fields and validate_query_scope pattern effectively prevent fabrication when data is absent.

When to use which:

• Use unicefstats-mcp for UNICEF child development analysis — it's simpler, faster, and far more accurate
• Use sdmx-mcp when you need to query non-UNICEF SDMX registries, explore dataflow structures, or work with hierarchical codelists

Full 3-way benchmark (LLM alone vs unicefstats-mcp vs sdmx-mcp): examples/results/

Quick Start

pip install unicefstats-mcp

Claude Code

Add to ~/.claude/.mcp.json:

{
  "mcpServers": {
    "unicefstats": {
      "command": "unicefstats-mcp"
    }
  }
}

Cursor / VS Code

Add to your MCP settings:

{
  "unicefstats": {
    "command": "unicefstats-mcp"
  }
}

Tools

Tool	Purpose	API call?
search_indicators(query, limit)	Find indicators by keyword	No
list_categories()	Browse thematic groups (CME, NUTRITION, EDUCATION, ...)	No
list_countries(region)	List countries with ISO3 codes	No
get_indicator_info(code)	Full metadata, SDMX details, available disaggregations	No
get_temporal_coverage(code)	Available year range and country count	Yes (lightweight)
get_data(indicator, countries, ...)	Fetch observations with optional disaggregation filters	Yes
get_api_reference(language, function)	unicefdata package API reference (Python/R/Stata)	No
get_server_metadata()	Server identity, version, provenance, data source	No

Workflow

1. search_indicators("child mortality")     → find indicator codes
2. get_indicator_info("CME_MRY0T4")         → check disaggregations & SDMX details
3. get_temporal_coverage("CME_MRY0T4")      → check year range
4. get_data("CME_MRY0T4", ["BRA", "IND"])   → fetch data
5. get_api_reference("python", "unicefData") → get code template to continue in a script

Resources

The server exposes six MCP resources clients can load for guidance and reference data:

URI	Purpose
unicef://system-prompt	Recommended system prompt — operating loop + temporal-frontier check + anti-extrapolation directive (load at session start)
unicef://llm-instructions	Full DO/DON'T rules, common mistakes, and anti-fabrication guidance
unicef://context	Runtime context — current_date / current_year for temporal-query sanity checks
unicef://categories	All indicator categories with counts
unicef://countries	ISO3 codes and country names
unicef://glossary	Disaggregation codes and indicator-prefix legend

The system-prompt and context resources address the T2 hallucination failure mode (model fabricating values for years beyond the data frontier). Pattern adopted from the World Bank data360-mcp server. See CHANGELOG entry for v0.5.0.

Demo

Step 1: Search for indicators

>>> search_indicators("stunting", limit=3)

{
  "query": "stunting",
  "total_matches": 11,
  "showing": 3,
  "results": [
    {"code": "FD_STUNTING", "name": "Moderate and severe stunting (Functional difficulties)"},
    {"code": "NT_ANT_HAZ_NE2", "name": "Height-for-age <-2 SD (stunting)"},
    {"code": "NT_ANT_HAZ_NE3", "name": "Height-for-age <-3 SD (severe stunting)"}
  ],
  "tip": "Use get_indicator_info('FD_STUNTING') for full details including available disaggregations."
}

Step 2: Get indicator metadata

>>> get_indicator_info("CME_MRY0T4")

{
  "code": "CME_MRY0T4",
  "name": "Under-five mortality rate",
  "description": "Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births",
  "dataflow": "GLOBAL_DATAFLOW",
  "sdmx_api": "https://sdmx.data.unicef.org/ws/public/sdmxapi/rest/data/UNICEF,GLOBAL_DATAFLOW,1.0/.CME_MRY0T4?format=csv",
  "disaggregation_filters": {
    "sex": ["_T (Total)", "M (Male)", "F (Female)"],
    "wealth_quintile": ["Q1 (Lowest)", "Q2", "Q3", "Q4", "Q5 (Highest)"],
    "residence": ["_T (Total)", "U (Urban)", "R (Rural)"]
  }
}

Step 3: Check temporal coverage

>>> get_temporal_coverage("CME_MRY0T4")

{
  "code": "CME_MRY0T4",
  "start_year": 1931,
  "end_year": 2024,
  "latest_year": 2024,
  "countries_with_data": 249,
  "note": "Not all countries have data for all years. Coverage varies by country."
}

Step 4: Fetch data

>>> get_data("CME_MRY0T4", ["BRA", "IND", "NGA"], start_year=2018, end_year=2023)

{
  "indicator": "CME_MRY0T4",
  "countries_requested": ["BRA", "IND", "NGA"],
  "total_rows_available": 18,
  "rows_returned": 18,
  "rows_truncated": false,
  "format": "compact",
  "summary": {
    "value_range": {"min": 14.42, "max": 117.56, "mean": 54.78},
    "year_range": {"earliest": 2018, "latest": 2023},
    "countries_in_result": 3
  },
  "data": [
    {"iso3": "BRA", "country": "Brazil",  "period": 2018, "indicator": "CME_MRY0T4", "value": 15.22},
    {"iso3": "BRA", "country": "Brazil",  "period": 2019, "indicator": "CME_MRY0T4", "value": 15.03},
    {"iso3": "BRA", "country": "Brazil",  "period": 2020, "indicator": "CME_MRY0T4", "value": 14.87},
    {"iso3": "BRA", "country": "Brazil",  "period": 2021, "indicator": "CME_MRY0T4", "value": 14.72},
    {"iso3": "BRA", "country": "Brazil",  "period": 2022, "indicator": "CME_MRY0T4", "value": 14.59},
    {"iso3": "BRA", "country": "Brazil",  "period": 2023, "indicator": "CME_MRY0T4", "value": 14.42},
    {"iso3": "IND", "country": "India",   "period": 2018, "indicator": "CME_MRY0T4", "value": 36.87},
    {"iso3": "IND", "country": "India",   "period": 2019, "indicator": "CME_MRY0T4", "value": 34.86},
    {"iso3": "IND", "country": "India",   "period": 2020, "indicator": "CME_MRY0T4", "value": 32.98},
    {"iso3": "IND", "country": "India",   "period": 2021, "indicator": "CME_MRY0T4", "value": 31.19},
    {"iso3": "IND", "country": "India",   "period": 2022, "indicator": "CME_MRY0T4", "value": 29.53},
    {"iso3": "IND", "country": "India",   "period": 2023, "indicator": "CME_MRY0T4", "value": 27.99},
    {"iso3": "NGA", "country": "Nigeria", "period": 2018, "indicator": "CME_MRY0T4", "value": 117.19},
    {"iso3": "NGA", "country": "Nigeria", "period": 2019, "indicator": "CME_MRY0T4", "value": 117.37},
    {"iso3": "NGA", "country": "Nigeria", "period": 2020, "indicator": "CME_MRY0T4", "value": 117.42},
    {"iso3": "NGA", "country": "Nigeria", "period": 2021, "indicator": "CME_MRY0T4", "value": 117.56},
    {"iso3": "NGA", "country": "Nigeria", "period": 2022, "indicator": "CME_MRY0T4", "value": 117.46},
    {"iso3": "NGA", "country": "Nigeria", "period": 2023, "indicator": "CME_MRY0T4", "value": 116.82}
  ]
}

Key insights an AI assistant would extract from this:

• Brazil: 14.4 per 1,000 — steadily declining, on track for SDG 3.2 target (≤25)
• India: 28.0 per 1,000 — rapid improvement (37→28 in 5 years), recently crossed SDG target
• Nigeria: 117 per 1,000 — essentially flat, 4.7× the SDG target, highest burden

Step 5: Get code template to continue in a script

>>> get_api_reference("r", "unicefData")

{
  "language": "r",
  "install": "install.packages(\"unicefdata\")",
  "import": "library(unicefdata)",
  "function": "unicefData",
  "signature": "unicefData(\n    indicator = NULL,        # character — indicator code(s)\n    countries = NULL,         # character vector — ISO3 codes, NULL = all\n    year = NULL,              # numeric, character (\"2015:2023\"), or vector\n    sex = \"_T\",               # character — \"_T\", \"M\", \"F\"\n    totals = FALSE,           # logical — only return aggregate totals\n    tidy = TRUE,              # logical — standardize column names\n    country_names = TRUE,     # logical — add country name column\n    format = \"long\",          # character — \"long\", \"wide\", \"wide_indicators\"\n    latest = FALSE,           # logical — most recent value per country\n    circa = FALSE,            # logical — closest available year\n    add_metadata = NULL,      # character vector — e.g. c('region', 'income_group')\n    dropna = FALSE,           # logical — drop rows with missing values\n    simplify = FALSE,         # logical — minimal columns\n    mrv = NULL,               # integer — most recent N values per country\n    raw = FALSE,              # logical — all disaggregations, no filtering\n)",
  "returns": "tibble with columns: indicator_code, iso3, country, period, value, sex, age, wealth_quintile, residence, ...",
  "examples": [
    {"description": "Under-5 mortality for Brazil, India, Nigeria (2015–2023)", "code": "df <- unicefData(\"CME_MRY0T4\", countries = c(\"BRA\", \"IND\", \"NGA\"), year = \"2015:2023\")"},
    {"description": "Latest stunting data for all countries", "code": "df <- unicefData(\"NT_ANT_HAZ_NE2\", latest = TRUE)"},
    {"description": "Wide format with region metadata", "code": "df <- unicefData(\"CME_MRY0T4\", format = \"wide\", add_metadata = c(\"region\", \"income_group\"))"}
  ]
}

This lets the AI generate correct R/Python/Stata code using the exact parameter names and syntax — no guessing from training data.

get_data parameters

Parameter	Type	Default	Description
indicator	str	required	Indicator code
countries	list[str]	required	ISO3 codes (max 30)
start_year	int	None	Start of year range
end_year	int	None	End of year range
sex	str	"_T"	"_T" (total), "M" (male), "F" (female)
wealth_quintile	str	None	"Q1"–"Q5", "B20", "B40", "T20"
residence	str	None	"U" (urban), "R" (rural), "_T" (total)
format	str	"compact"	"compact" (5 cols) or "full" (all cols)
limit	int	200	Max rows (1–500)

Response features

• summary: Value range (min/max/mean), year range, country count
• disaggregations_in_data: Which dimensions have non-trivial variation
• total_rows_available vs rows_returned: Pagination metadata
• tip: Contextual guidance for next steps or narrowing results

Prompts

compare_indicators

Pre-built analysis workflow: fetches indicator metadata and data, then produces a structured comparison.

compare_indicators(indicator="CME_MRY0T4", countries="BRA,IND,NGA", start_year="2015", end_year="2023")

write_unicefdata_code

Generate runnable Python, R, or Stata code using the unicefdata package. The AI will call get_api_reference() to get the exact function signatures, then write code matching the user's task.

write_unicefdata_code(
    task="Compare under-5 mortality for Brazil and India, 2015-2023, then plot the trends",
    language="r"
)

This bridges the gap between conversational exploration (via MCP tools) and reproducible analysis scripts (via unicefdata packages).

Benchmark Results

We benchmarked the MCP against a bare LLM (Claude Sonnet 4, no tools) using the EQA metric from Azevedo (2025). 300 queries across 10 indicators, 20 countries, 2 prompt types, and 2 hallucination test categories.

Headline numbers

Metric	LLM alone	LLM + MCP	Improvement
EQA ("latest" prompt)	0.172	0.984	5.7×
EQA ("direct" prompt)	0.121	0.995	8.2×
Indicators at EQA >= 0.95	0/10	10/10	—
T1 hallucination (gap years)	9%	7%	-2pp
T2 hallucination (never existed)	11%	37% raw / ~10% corrected	See analysis
Cost per query	$0.003	$0.018	6×

EQA decomposition (baseline_latest prompt)

Component	LLM alone	LLM + MCP	Gain
ER (extraction rate)	0.50	1.00	+0.50
YA (year accuracy)	0.24	0.99	+0.75
VA (value accuracy)	0.37	1.00	+0.63
EQA = ER × YA × VA	0.147	0.990	+0.843

Key findings

1. All 10 indicators at EQA >= 0.95 with MCP, replicated across 40 countries (R1 + R2 with zero overlap). 7 of 10 achieve perfect EQA = 1.000.

2. Year accuracy is the bare LLM's biggest weakness (YA = 0.24). It cites 2021-2022 as "latest" when IGME 2024 estimates exist. The MCP queries the API and returns the actual latest year.

3. The direct prompt shows larger MCP gain (+0.722 vs +0.613) because it eliminates YA and isolates pure retrieval accuracy.

4. T2 hallucination (~37%) is inflated by ground truth misclassification: the SDMX API has IGME mortality data for micro-states that the ground truth pipeline missed. After correction: MCP ~10%, LLM alone ~5%. The remaining hallucination is driven by the confidence effect — Claude overrides tool errors when it has strong domain priors.

5. The confidence effect: When the MCP tool returns "no data" but the LLM has strong domain priors (e.g., child mortality for well-known countries), it overrides the tool and fabricates anyway. This is a fundamental LLM behavior, not MCP-specific.

3-way comparison (vs sdmx-mcp)

Metric	LLM alone	unicefstats-mcp	sdmx-mcp
EQA (all positive)	0.147	0.990	0.074
T1 hallucination	9%	7%	0%
T2 hallucination	11%	37%	0%
Cost (300 queries)	$0.89	$5.47	$26.20
Avg latency	5s	9.8s	60s

sdmx-mcp's raw SDMX-JSON output is hard for LLMs to parse (VA = 0.11), but its anti-hallucination guardrails are highly effective (0% fabrication). See Relationship to sdmx-mcp for details.

Full analysis, per-indicator decomposition, and methodology: examples/RESULTS.md

Benchmark data (parquet with full LLM responses): examples/results/

Benchmark design rationale: examples/DESIGN_ISSUES.md

Reproducing the benchmark

# Build ground truth from UNICEF SDMX API
python examples/00_build_ground_truth.py

# Run 200-query benchmark (requires ANTHROPIC_API_KEY, ~$6)
python examples/benchmark_eqa.py

# Add 100 direct-prompt queries to existing run (~$3)
python examples/01_run_direct_supplement.py

Citation

This benchmark uses the EQA metric from:

Azevedo, J.P. (2025). "AI Reliability for Official Statistics: Benchmarking Large Language Models with the UNICEF Data Warehouse." UNICEF Chief Statistician Office. github.com/jpazvd/unicef-sdg-llm-benchmark-dev

Deployment

Local (stdio)

unicefstats-mcp

Remote (SSE)

unicefstats-mcp --transport sse --port 8000

Docker

docker build -t unicefstats-mcp .
docker run -p 8000:8000 unicefstats-mcp

Development

pip install -e ".[dev]"
pytest tests/ -v
ruff check src/ tests/
mypy src/unicefstats_mcp/

Contributing

Contributions are welcome.

Ways to contribute

• Bug reports: Open an issue with steps to reproduce
• Feature requests: Suggest new tools, indicators, or output formats via issues
• Code: Fork, branch, submit a PR — see development setup below
• Benchmark: Run the EQA benchmark on different models and share results
• Documentation: Improve examples, fix typos, add use cases

Development setup

git clone https://github.com/jpazvd/unicefstats-mcp.git
cd unicefstats-mcp
pip install -e ".[dev,benchmark]"
pytest tests/ -v
ruff check src/ tests/
mypy src/unicefstats_mcp/

Pull request guidelines

1. One concern per PR — keep changes focused and reviewable
2. Include tests for new tools or bug fixes
3. Run the linter (ruff check) and type checker (mypy) before submitting
4. Update the README if you change tool signatures or add new features
5. Do not commit API keys or benchmark result parquets larger than 500KB

Priority areas

See the audit findings for known issues. High-impact areas:

• MNCH dataflow bug: MNCH_CSEC and MNCH_BIRTH18 return 0 EQA due to a dataflow resolution issue in the unicefdata package
• T2 hallucination reduction: Further reduce fabrication when API returns no results (currently ~10%; see Limitations)

Limitations and Hallucination Risks

Data limitations

• Coverage is uneven across indicators, countries, and years. Survey-based indicators (nutrition, education, protection) have 3-5 year gaps between data points by design.
• Mortality indicators (CME_*) are modeled estimates from the UN Inter-agency Group (IGME), with uncertainty intervals not surfaced in compact output.
• Not all indicators support all disaggregation dimensions; get_indicator_info() lists what's available per indicator.
• get_data() caps at 500 rows per call.

Hallucination risks

Benchmark testing (600 queries pooled across two replication samples, 10 indicators, 45 countries) identified two patterns:

Type	Description	Rate	Mitigation
T1 (gap-year)	LLM cites a year when data exists but for a different year	~7%	Server returns the actual year; LLM occasionally ignores it
T2 (forward-of-frontier)	LLM fabricates a value for a year beyond the data frontier	~36%	v0.5.0 ships an anti-extrapolation system prompt (unicef://system-prompt) and runtime context (unicef://context). Load these at session start.

T2 is the dominant risk — driven by a "confidence effect" where the LLM, having retrieved adjacent-year data, extrapolates forward. The v0.5.0 system prompt names the failure mechanism and lists forbidden phrases ("approximately", "projected", "based on the trend") so the directive cannot be satisfied by hedged forecasts. Skill / system-prompt enforcement is structural; tool-description guidance is advisory.

Full benchmark methodology: examples/RESULTS.md

Provenance and Ownership

All data served by this MCP originates from the UNICEF Data Warehouse, accessed live via the public SDMX REST API. No observation data is stored or cached — every get_data() call results in a live SDMX request. The indicator and country registries are cached in memory at first access for performance; these are catalogue metadata, not statistical values. The MCP reformats output for LLM consumption but does not alter values.

All releases are published from GitHub Actions using PyPI Trusted Publishing (OIDC). No long-lived API tokens exist. Release provenance is verifiable via PyPI attestations.

For full details on data origin, ownership, distribution pipeline, and interpretation caveats, see PROVENANCE.md.

How to Verify This MCP

Check	How
Source	Repository is jpazvd/unicefstats-mcp on GitHub
Package	pip show unicefstats-mcp — verify Home-page points to the canonical repo
Version	python -c "import unicefstats_mcp; print(unicefstats_mcp.__version__)" — compare with server.json and PyPI
Provenance	PyPI attestations link each release to a GitHub Actions workflow
Runtime	Call get_server_metadata() — returns canonical name, version, publisher, and data source

License

MIT