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

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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

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:

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

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

International Organizations

National Statistics Offices

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:

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

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:

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

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 (300-query benchmark, v0.5.x)

v0.7.1 same-day clean reproduction (n=500, 2026-05-08)

After v0.7.0 shipped the indicator-name resolver, we re-ran a 500-query subset (100 POSITIVE + 200 T1 + 200 T2) on the per-wave checkpoint architecture (PR #53), with the v0.6.4 baseline run same-day to control for upstream-model snapshot drift:

A-side EQA was within 0.3 pp across the two runs, confirming the same-day discipline worked: the B-side delta is real, not snapshot drift. The v0.7.1 reproduction confirms the original 6.7×/8.2× accuracy headline at 7×, and shows that the v0.4.0 safety layer + v0.7.0 indicator resolver brought T2 fabrication from 37% (v0.3.0) down to 13% — but the residual ~11 pp gap relative to the no-tools baseline appears structural, matching what the broader tool-augmented LLM and RAG literature documents (see Limitations).

EQA decomposition (baseline_latest prompt)

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)

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:

T2 was historically the dominant risk — driven by a confidence effect where the LLM, having retrieved adjacent-year data, extrapolates forward. The v0.4.0 safety layer + v0.5.0 system prompt + v0.7.0 indicator resolver brought combined T1+T2 fabrication from 37% (v0.3.0) down to 13% (v0.7.1, same-day clean baseline) — a ~24 pp reduction.

The residual ~11 pp gap relative to the no-tools baseline (2%) appears structural, not a bug we have not yet fixed. This finding aligns with what the broader tool-augmented LLM and RAG literature has been documenting in parallel:

• The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination (ICLR 2025) — shows the relationship is causal: as models get better at tool use, tool hallucination rises proportionally with capability.

• Reducing Tool Hallucination via Reliability Alignment (Cao et al., 2024, arXiv:2412.04141) — formalises the failure as tool-selection errors (wrong tool, failed refusal) and tool-usage errors (fabricated parameters).

• ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability (Sun et al., 2024) — shows mechanistically that an LLM's parametric knowledge can override retrieved context inside the residual stream.

The takeaway for users: server-side guardrails reduce the magnitude of tool-augmented hallucination; they do not, on current evidence, change the direction. Any production deployment should:

1. Load the unicef://system-prompt and unicef://context resources at session start (handles forward-of-frontier fabrication).

2. Treat MCP results as best-effort retrieval, not infallible truth — verify load-bearing values against the UNICEF Data Warehouse before citing.

3. Prefer queries with explicit years ("under-five mortality in Nigeria in 2023") over open-ended ones ("the latest under-five mortality in Nigeria") — the former triggers refusal more reliably when data is absent.

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

License

MIT