How do I install NHANES (design Correct Survey Analysis)?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does NHANES (design Correct Survey Analysis) need an API key?

The publisher lists these environment variables: NHANES_MCP_CACHE, NHANES_MCP_DATA_DIR.

What transport does NHANES (design Correct Survey Analysis) use?

NHANES (design Correct Survey Analysis) runs locally over stdio.

When was NHANES (design Correct Survey Analysis) last checked?

Last checked 2026-11-08. Design-correct NHANES analysis: survey weights, pooled cycles, CIs and NCHS reliability flags.

NHANES (design Correct Survey Analysis) MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx nhanes-mcp==0.5.1

Credentials from the publisher

NHANES_MCP_CACHE — Folder for downloaded NHANES files (default ~/.cache/nhanes-mcp)
NHANES_MCP_DATA_DIR — Folder of manually downloaded .xpt files, for offline use

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1NHANES_MCP_CACHE=<value> NHANES_MCP_DATA_DIR=<value> claude mcp add com.blackswancausallabs-nhanes-mcp -- uvx nhanes-mcp==0.5.1

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "com.blackswancausallabs-nhanes-mcp": {4      "command": "uvx",5      "args": [6        "nhanes-mcp==0.5.1"7      ],8      "env": {9        "NHANES_MCP_CACHE": "<value>",10        "NHANES_MCP_DATA_DIR": "<value>"11      }12    }13  }14}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version: 0.5.1
API key

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

nhanes-mcp

An MCP server for design-correct, conversational access to NHANES public-use data. Black Swan Causal Labs · MIT license · v0.5 · PyPI · MCP Registry

Most "chat with a dataset" layers let an agent compute an unweighted mean. With NHANES that answer is wrong. This server makes the defensible analysis the default: the agent asks a question in plain language, and the server finds the files, merges them on SEQN, picks the right weight, keeps the full survey design, and reports design-based estimates with NCHS reliability flags.

Not affiliated with, or endorsed by, NCHS or CDC. Data are the public-use NHANES files published by the National Center for Health Statistics and downloaded directly from cdc.gov. Users are responsible for following the NCHS data use agreement.

What it handles for you

Pitfall	What the server does
Wrong / no weight	`build_dataset` picks the most restrictive weight (interview → MEC → fasting / phlebotomy / surplus-serum subsample → dietary day-1/day-2), explains why, and stores it in `WT_ANALYSIS`. Overrides (`build_dataset(weight=...)`, `set_weight`) are reported with every result; `WT_ANALYSIS` cannot be overwritten silently
Subsetting before estimation	`domain=` expressions keep the full design (zero weight outside the domain)
Pooling cycles	Weights rescaled by cycle years / total years (2017–March 2020 counts as 3.2 years); 1999–2002 uses 4-year weights; strata made cycle-unique; refuses 2017–2018 + 2017–2020 overlap
Long-format tables (e.g. prescriptions)	Refuses to join tables with repeated `SEQN` (which would silently duplicate weights); `flag_from_long_table` collapses them to one row per person
Refused / don't-know codes	`describe_variable` reads the CDC codebook and suggests sentinel codes; `set_missing` recodes them
Silent 0 for missing	`derive_variable` propagates missingness (`any` / `all` / `none`); `coalesce`, `fillna`, `isna`, `notna`, `where` handle missingness deliberately
Irregular file names	Tries known variants (e.g. 1999–2000 surplus-serum files `SSCMV_A`, `SSMUMP_A`)
Variance	Taylor linearization, strata × PSU, design df; Korn–Graubard CIs and NCHS 2017 reliability flags for proportions
Age adjustment	Direct adjustment to the 2000 US standard (20–39 / 40–59 / 60+) with linearized SE, or to any caller-supplied standard (age groups + population), with a warning for in-domain records outside the groups
Mortality	Optional join of the public-use Linked Mortality File (follow-up through 2019) and a design-based Cox model

Tools (17)

Step	Tools
Orient	`list_cycles`, `analysis_guidance`
Find	`list_files`, `search_variables`, `describe_variable`
Build	`build_dataset` (optional mortality join), `describe_dataset`
Clean / derive	`set_missing`, `derive_variable`, `flag_from_long_table`, `set_weight`
Analyze	`survey_frequency`, `survey_estimate`, `survey_regression` (linear / logistic), `survey_cox` (Cox PH, Binder variance)
Present	`show_results` — text + structured results; interactive view with the optional Results Explorer add-on
Export	`export_dataset`

Cycles: 1999–2000 through 2017–2018, 2017–March 2020 (pre-pandemic, P_ files) and August 2021–August 2023.

Results Explorer (optional add-on)

show_results returns design-based results as text plus structured data in every client. With the optional NHANES Results Explorer add-on installed, MCP Apps hosts (Claude Desktop/web, ChatGPT, VS Code, Goose) also render an interactive view: headline estimate with CI and NCHS reliability badge, a crude / age-adjusted toggle that re-runs the estimate on the server, the analysis plan, subgroup panels, server warnings, benchmarks against published estimates, and design provenance.

The add-on is a separate package from Black Swan Causal Labs under the PolyForm Noncommercial License 1.0.0 (free for academic, public-health and other noncommercial use; commercial use needs a license — https://blackswancausallabs.com). It is not part of this MIT repository. nhanes-mcp finds it if it is installed in the same Python environment, if NHANES_MCP_EXPLORER_PATH points to it, or if a folder named nhanes-mcp-explorer sits next to the nhanes-mcp folder.

Validation

Estimates were checked against published NCHS results (validation/):

Prevalence: 57 of 57 published NCHS estimates reproduced (Data Briefs 360, 363, 508, 515; pooled 2015–2018; 2017–March 2020 pre-pandemic).
Standard errors: 20 of 20 match; 11 of 12 published 95% CI bounds identical (the 12th differs by 0.1 at a rounding edge).
Mortality: a design-based Cox model on NHANES 1999–2006 (adults 25+) reproduces 6 of 7 published hazard ratios within their CIs (NHSR 155). The Mexican American contrast does not reproduce (0.71 vs 1.12 published); this is under investigation and the linked file here has longer follow-up (2019 vs 2015).
Hypertension (NCHS Data Brief 511, 2021–2023, adults 18+): prevalence (crude and age-adjusted, by sex and age), awareness, treatment and control — 17 of 17 published estimates reproduced exactly.
CMV seroprevalence (Bate et al., Clin Infect Dis 2010; NHANES 1999–2004, ages 6–49, surplus-serum weights): see tests/benchmark_nchs.py. Before v0.4 the server silently used MEC weights here and could not load the 1999–2000 file.
Unit tests (tests/): variance checked against an independent loop implementation and a delete-one-PSU jackknife; Cox model checked against statsmodels PHReg and a jackknife; weight selection, pooling, guards, expression semantics, long-table and dietary-weight handling.

Install

Easiest: let your AI assistant do it

Paste this into Claude (Cowork or Claude Code), or any agent that can run commands on your computer:

Install the nhanes-mcp MCP server (PyPI package nhanes-mcp) for Claude Desktop. Install uv if it is missing, then add this entry to my Claude Desktop config (claude_desktop_config.json), using the full path to uvx: "nhanes": {"command": "uvx", "args": ["nhanes-mcp"]}. Keep my existing servers. Then tell me to restart Claude Desktop.

One line in the config (uvx)

With uv installed, add to claude_desktop_config.json and restart Claude Desktop (on macOS use the full path from which uvx, e.g. /Users/<you>/.local/bin/uvx):

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

uvx fetches the server from PyPI into an isolated environment on first launch; no clone or virtual environment to manage. To run the latest code from GitHub instead, use "args": ["--from", "git+https://github.com/Black-Swan-Causal-Labs/nhanes-mcp", "nhanes-mcp"].

From source (for development)

python3 -m venv ~/.nhanes-mcp-venv
~/.nhanes-mcp-venv/bin/pip install "mcp>=1.2,<2" pandas numpy scipy pyreadstat httpx beautifulsoup4 lxml
git clone https://github.com/Black-Swan-Causal-Labs/nhanes-mcp.git ~/nhanes-mcp

"nhanes": {
  "command": "/Users/<you>/.nhanes-mcp-venv/bin/python",
  "args": ["-m", "nhanes_mcp"],
  "env": {"PYTHONPATH": "/Users/<you>/nhanes-mcp"}
}

Any MCP client that runs local stdio servers works the same way. Data are downloaded from cdc.gov on first use and cached in ~/.cache/nhanes-mcp (override with NHANES_MCP_CACHE). Set NHANES_MCP_DATA_DIR to a folder of manually downloaded .xpt files to work offline.

Need help setting it up for your team, or adapting it to another survey or dataset? Contact Black Swan Causal Labs.

Tests

python tests/test_offline.py            # synthetic NHANES-shaped data, no network
python tests/test_cox.py
python tests/test_long_and_dietary.py
python tests/benchmark_nchs.py          # reproduces published NCHS estimates (needs network)

Known open issues

Age-adjusted adult obesity for 2009–2010 and earlier runs 0.1–0.7 points below NCHS Health E-Stat 111 (2011–2012 onward matches exactly). Pooling and pregnancy-code handling were ruled out; cause under investigation.
The CMV analysis finds 14,198 tested participants aged 6–49 in the public surplus-serum files versus 15,310 reported by Bate et al.; unexplained.
NHANES III (1988–1994) is not supported.

Changelog

0.5.1 — First release on PyPI (uvx nhanes-mcp) and the official MCP Registry (com.blackswancausallabs/nhanes-mcp). Packaging metadata only; no analysis changes.
0.5.0 — show_results tool (text + structured results; interactive view via the optional Results Explorer add-on); one-command install with uvx; analysis guidance rule 11.
0.4.1 — Comparisons inside missing-aware functions now return missing when an operand is missing, so skip-pattern definitions such as where(BPQ020 == 1, fillna(BPQ150, 2) == 1, 0) are missing (not 0) for people never asked the screener. Hypertension benchmark (NCHS Data Brief 511) added.
0.4.0 — Surplus-serum and other file-specific subsample weights with 2Y/4Y suffixes are detected and pooled; 1999–2000 _A file names resolved; build_dataset(weight=...) and new set_weight tool, both recorded in every result; design columns protected from derive_variable; missing-aware expression functions; custom age standards; CMV benchmark added.
0.3.0 — Initial public release.

Limitations

Public-use files only. Restricted-use data (including the NHANES–CMS Medicare/Medicaid linkage) require an NCHS Research Data Center.
Variance uses Taylor linearization with PSUs treated as sampled with replacement, as NCHS recommends; replicate weights are not used.
The server reports what the data support; it does not choose a study design for you.

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install NHANES (design Correct Survey Analysis)?

Does NHANES (design Correct Survey Analysis) need an API key?

What transport does NHANES (design Correct Survey Analysis) use?

When was NHANES (design Correct Survey Analysis) last checked?

Documentation

nhanes-mcp

What it handles for you

Tools (17)

Results Explorer (optional add-on)

Validation

Install

Easiest: let your AI assistant do it

One line in the config (uvx)

From source (for development)

Tests

Known open issues

Changelog

Limitations