census-mcp-server

Reviewed Jan 1, 2000.

• tools: 8 tools scanned
• metadata: scanned
• packages: 2 checked

No findings.

Tools (8)

• census_list_datasets

  Browse available Census Bureau datasets with their supported vintage years. Use as the starting point when the right dataset is unknown — ACS5, ACS1, and their profile, subject, and comparison tables, population estimates, the decennial census files, and the business datasets (County Business Patterns, Economic Census, Nonemployer Statistics) serve different use cases. Pass the dataset_id value to the dataset parameter in other census tools. Each description names the predicates a dataset requires and the geography levels it publishes, both of which vary by dataset.

• census_list_geographies

  List the geography levels available for a given Census dataset and year, along with the parent geographies each level requires. Use before querying to confirm that the target geography level exists — ACS1 omits many sub-state levels, and not all datasets support tracts or block groups. The geography_level values returned here are the valid inputs to the geography_level parameter in census_query_data and census_compare_geographies.

• census_search_variables

  Search Census variables by keyword across variable labels and concept groups. Returns variable codes with human-readable labels — use this to go from a concept like "median household income" to the variable code B19013_001E needed for data queries. On ACS datasets it returns both estimate (E suffix) and margin-of-error (M suffix) codes so you can request both; the ACS comparison profiles (acs/acs5/cprofile, acs/acs1/cprofile) and the other dataset families publish no margins of error. Also use it to find the predicate codes a dataset filters on, such as NAICS2017 in cbp. Adding a word narrows the results, since every word must match; when totalMatches exceeds the limit, a more specific query reaches the rest.

• census_get_variable

  Fetch full metadata for one or more Census variable codes — label, concept group, predicate type, the table's universe, and margin-of-error sibling references. Use to confirm a variable code before building a query, or to look up what a known code means. On ACS datasets it returns estimate_code and moe_code sibling references so you can request both without a separate search, and a margin-of-error code carries attribute_of and attribute_type MARGIN_OF_ERROR as the Census publishes them; the ACS comparison profiles (acs/acs5/cprofile, acs/acs1/cprofile) and the other dataset families publish no margins of error and carry none of these fields. It also resolves the annotation and flag columns the data tools accept, such as B19013_001EA or EMP_F, naming the column each one belongs to, and predicate codes such as NAICS2017 or SEX, confirming a filter dimension exists in a dataset before a query uses it — for the values a dimension accepts rather than the dimension itself, call census_list_p

• census_list_predicate_values

  List the codes a Census filter dimension accepts, so a predicates map can be written without guessing. Answers the question left open when census_query_data or census_compare_geographies reports that a dimension was left unset. Which route a dimension takes depends on the vintage: NAICS and POPGROUP always publish a value list in the dataset dictionary, and on the current vintages EMPSZES, LFO, RCPSZES, TAXSTAT, and TYPOP publish none and are enumerated here against the live data endpoint instead. A dictionary value list is a classification shared across Census products rather than a list of what one dataset serves, and roughly half of its codes typically return no rows anywhere — those are checked against the dataset's own published rows and dropped, and the response source field says whether that check ran. The dictionary lists run to thousands of codes and are best narrowed with query. Pass the returned code as the dimension's value in predicates.

• census_resolve_geography

  Resolve a place name, ZIP code, or street address to Census FIPS identifiers. Converts names like "King County, WA", "Seattle, WA", or "Seattle-Tacoma-Bellevue, WA", and ZIPs like "98109", to the codes required by census_query_data and census_compare_geographies. Use before querying when you have a place name rather than raw FIPS codes — state_fips maps to parent_fips and fips_summary maps to geography_fips in downstream tools, and geography_type is itself the geography_level to query at.

• census_query_data

  Query a Census dataset for one or more variables at a specific geography. Accepts FIPS codes for the target geography — use census_resolve_geography to convert place names to FIPS when needed. On ACS datasets, labeled estimates and margin-of-error values are returned together (the comparison profiles publish no margins), and the negative sentinel values the Census writes for an estimate or margin of error it cannot publish are decoded into the meanings the Census gives them rather than passed through as raw numbers. A value cbp, ecnbasic, or nonemp withheld is stored as 0 beside a flag, and is reported as withheld, with the meaning of its flag, rather than as a zero. Pass geography_fips as "*" for every geography at the level within the parent: rows come back in GEOID order, up to limit per call (default 50, max 500), with totalCount giving how many matched and offset reaching the rest — the order is not a ranking, so use census_compare_geographies to rank. On the business datasets (cb

• census_compare_geographies

  Compare one or more variables across multiple geographies at the same level — all counties in a state, all states nationally, or a named set of specific geographies — ranked on the value of one of them. Covers queries like "compare median income across WA counties" or "which states have the most people below the poverty line." A count ranks geographies by size, not by rate, so to rank a rate, rank a published percentage: S1701_C03_001E (percent below the poverty level, dataset acs/acs5/subject), DP03_0128PE (the same percentage, acs/acs5/profile), or DP04_0047PE (percent of occupied housing units that are renter-occupied, acs/acs5/profile). Profile and subject tables reach tracts but not block groups. Omit within to compare all geographies nationally at the level. Suppressed values are decoded to human-readable labels rather than passed through as raw negative sentinels. On the business datasets (cbp, ecnbasic, nonemp), pep/charv, dec/ddhca, and acs/acs1/spp, use predicates to rank wit