NHANES (design-correct survey analysis) MCP server

Read from the package source without running it. The installed server may list more.

• 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.

• derive_variablewrite action

  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), i

• 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_datasetwrite action

  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.

• 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": [[

• survey_frequency

  Weighted distribution of a categorical variable (Table 1 style): for each level, unweighted n, weighted percent, SE and Korn-Graubard CI, within the domain and optionally by group. labels: optional {code: label} map, e.g. {"1": "Male", "2": "Female"}.

• survey_regression

  Survey-weighted linear ('gaussian') or logistic ('binomial') regression with design-based SEs. Categorical predictors must be dummy-coded first with derive_variable.

• Code scan7 source files scanned25/25
• –Live reliabilityno gateway calls yet and no remote to proben/a
• –Tool poisoningtools not inspected (local package is not executed); not countedn/a
• Auth qualitylocal package, no credentials required12/15
• Maintenancelast push 9 days ago15/15
• Maintainer identitynamespace and repository owner differ; website matches verified namespace6/10

Safety score

89/100

higher than 80% of 1,337 Media servers

Tools

17

the typical Media server has 7

Tools that change things

12%

typical: 4%. Fewer write tools means less an agent can break

Downloads a week

30

more than 15% of 365 Media servers with a package

NHANES (design-correct survey analysis): common questions

Is NHANES (design-correct survey analysis) MCP server safe?

Yes, by our scan: it is graded A (89/100). Read the NHANES (design-correct survey analysis) safety report

How do I install NHANES (design-correct survey analysis)?

It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.

Does NHANES (design-correct survey analysis) need an API key?

No secret keys are declared. It reads 2 settings from the environment.

Is NHANES (design-correct survey analysis) maintained?

The last commit was 30 days ago (2026-10-05). The latest release is v0.5.1.

What can I use instead of NHANES (design-correct survey analysis)?

Servers from other publishers that do the same job: Dembrandt MCP server, KiCad MCP Pro MCP server and Figma MCP Server.

How does NHANES (design-correct survey analysis) compare with other Media MCP servers?

safety score 89/100 (higher than 80% of 1,337 Media servers); tools 17 (the typical Media server has 7); tools that change things 12% (typical: 4%. Fewer write tools means less an agent can break). Full comparison.