NHANES (design-correct survey analysis)

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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/blackswancausallabs-com-nhanes-mcp

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What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 26, 2026.

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7 read · 6 write · 0 delete · 4 not yet classified

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Python package

Tools (17)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• analysis_guidance

  Rules for valid NHANES estimation that this server enforces. Read before analyzing.

• build_dataset

  Build an analysis-ready dataset: DEMO universe (all participants, needed for valid domain estimation) left-joined to the requested tables on SEQN, for one or more cycles. tables: base names without cycle suffix, e.g. ["BMX", "TCHOL", "BPQ"] (DEMO is always included). variables: columns to keep from those tables (default: all). Weight/design variables are always kept. The analysis weight is chosen and rescaled automatically -> column WT_ANALYSIS. Subsample weights carried by a file (fasting WTSAF2YR, phlebotomy WTPH2YR, surplus serum WTSSMC2Y/4Y, ...) are detected. weight: optional weight kind…

• derive_variable

  Create a variable from an expression over existing columns, e.g. name='OBESE', expression='BMXBMI >= 30' (booleans become 0/1). missing: 'any' -> result missing if ANY referenced column is missing (default, conservative); 'all' -> missing only if ALL referenced columns are missing (for OR-type definitions); 'none' -> no propagation. Missing-aware functions: coalesce(a, b, ...), fillna(x, value), isna(x), notna(x), where(cond, a, b). Columns used only inside them are exempt from the missing rule. Design columns (WT_ANALYSIS, strata, PSU, SEQN, CYCLE) cannot be overwritten her…

• describe_dataset

  Columns, non-missing counts, weight choice, derived-variable definitions and warnings.

• describe_variable

  Codebook entry (label, question text, target population, value codes) plus observed distribution. Flags codes that look like refused/don't-know so they can be set to missing.

• export_dataset

  Write the analytic dataset (with WT_ANALYSIS, SDMVSTRA_U, SDMVPSU) to CSV plus a provenance sidecar.

• flag_from_long_table

  Add a per-person 0/1 indicator from a file with several rows per participant (e.g. RXQ_RX prescriptions, DR1IFF foods). Flag = 1 if ANY row's value in ANY of `columns` matches the regex `pattern` (case-insensitive), e.g. table='RXQ_RX', columns=['RXDRSC1','RXDRSC2','RXDRSC3'], pattern='^G40' for ICD-10 epilepsy as the reason for use. People absent from the file are missing.

• list_cycles

  List NHANES cycles this server supports, their file-name conventions and weight rules.

• list_files

  List data files for a cycle and component (Demographics, Dietary, Examination, Laboratory, Questionnaire).

• search_variables

  Search NHANES variable names/descriptions (regex, case-insensitive) across cycles/components. Defaults to the 2017-2018 cycle and all components.

• set_missing

  Recode sentinel values (e.g. 7, 9, 77, 99 for refused/don't know) to missing.

• set_weight

  Replace the analysis weight with a custom expression over existing columns, e.g. 'coalesce(WTSSMC4Y * 4/6, WTSSMC2Y * 2/6)'. Use only when build_dataset cannot select the right weight. The change is recorded and reported (weight kind 'CUSTOM' + a warning) in every later result. Negative or missing weights become 0 (out of sample).

• show_results

  Present design-based results: headline estimate, subgroup panels, the analysis plan (so the user can check how the question was interpreted), server warnings, NCHS benchmarks and provenance. Returns a text summary plus structured data; when the optional NHANES Results Explorer add-on is installed, MCP Apps hosts also render it as an interactive view. Same estimation arguments as survey_estimate (dataset_id, variable, statistic, domain, age_adjust); by: one panel per grouping variable, e.g. ['RIAGENDR', 'RIDRETH3', 'AGEGRP'] (age-adjustment is skipped for age groupings -- names starting with A…

• survey_cox

  Survey-weighted Cox proportional hazards model (Breslow ties, Binder linearized variance; same estimator as SUDAAN SURVIVAL / R svycoxph). Typical use with include_mortality=True: time='PERMTH_INT' (or PERMTH_EXM), event='MORTSTAT', domain including 'ELIGSTAT == 1'. Categorical predictors must be dummy-coded first with derive_variable.

• survey_estimate

  Design-based estimate (Taylor linearization) of a mean, proportion (0/1 variable) or total. domain: expression defining the subpopulation, e.g. 'RIDAGEYR >= 20 & RIDEXPRG != 1' (the design is NOT subset; out-of-domain records get zero weight). by: grouping variables, e.g. ['RIAGENDR']. age_adjust: a preset name ('nchs_adults_20plus': 2000 census, 20-39/40-59/60+) or a custom standard {"groups": [[6,11],[12,19],[20,29]], "population": [24.6, 32.5, 38.3]} (or "proportions"), optionally "age_var" (default RIDAGEYR) and "note" naming the source of the standard. Proportions come wit…

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33

Last commit

Oct 5, 2026 (28 days ago)

Last release

Oct 5, 2026 (28 days ago)

Last package publish

Oct 5, 2026 (28 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

com.blackswancausallabs/nhanes-mcp

Version

0.5.1

Auth

No auth

Packages

pypi nhanes-mcp

License

MIT

Repository

github.com/Black-Swan-Causal-Labs/nhanes-mcp

Website

blackswancausallabs.com/blog.html

First seen

Oct 24, 2026

Last checked

Nov 1, 2026

Source

Official MCP Registrygithub-graphql