census-mcp-server

endpoint

https://census.caseyjhand.com/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

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karma

0 · newcomer

Is census-mcp-server live?

Yes — it answered the hub's last check (checked 1h ago). It answered 98% of checks over the last 30 days.

Is census-mcp-server free to use?

Yes — the hub reached it with no key and no payment.

What tools does census-mcp-server have?

8 tools: census_query_data, census_resolve_geography, census_compare_geographies, census_get_variable, census_list_datasets, census_search_variables, census_list_predicate_values, census_list_geographies.

Is census-mcp-server safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• census_query_data reads unknown 13h ago

  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 (cbp, ecnbasic, nonemp), pep/charv, dec/ddhca, and acs/acs1/spp, use predicates to filter by industry, size class, or population group — a query that omits one is answered with a default the Census API picks, which is an all-categories total on some dimensions and a single category on others. Each row names the defaults that were applied in applied_filters, and census_list_predicate_values enumerates the codes a dimension accepts. One geography can also come back on more than one row: pep/charv publishes an April estimates base alongside its July estimate, and each row carries a record field saying which it is.

  mcp-tool

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "variables",
      "geography_level",
      "geography_fips"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year (default: latest available for the dataset)."
      },
      "limit": {
        "type": "integer",
        "maximum": 500,
        "minimum": 1,
        "description": "Most rows to return (default: 50, max: 500). Rows come in GEOID order, a geography's records or categories in code order, and each one counts, so a geography returned as several records (pep/charv April and July) or as one row per category of a \"*\" predicate takes one row each. totalCount says how many rows matched."
      },
      "offset": {
        "type": "integer",
        "maximum": 9007199254740991,
        "minimum": 0,
        "description": "Rows to skip before returning up to limit (default: 0). Pages run in GEOID order, so offset 50 with limit 50 returns rows 51–100, and the notice names the offset of the next page. An offset at or past totalCount returns no rows."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset to query (default: \"acs/acs5\"). Use census_list_datasets to discover valid values. Case is ignored, and a two-part code can be given by its last part alone — \"acs5\" is acs/acs5, \"pl\" is dec/pl. Three-part codes such as acs/acs5/profile must be given in full. The response echoes the resolved code."
      },
      "variables": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Variable codes to retrieve (e.g., [\"B19013_001E\", \"B19013_001M\"]). Codes are uppercased before the request, so \"b19013_001e\" reads as B19013_001E and the response is keyed by the uppercase code. At most 49 per call: the Census API accepts 50 columns per request and every query also sends NAME. On datasets where a label column is added for each filter dimension left unset, or record columns are added (cbp, ecnbasic, nonemp, pep/charv, dec/ddhca, acs/acs1/spp), the maximum is lower, and too_many_variables states the exact number for the query. Use census_search_variables to find codes. On ACS datasets only, apart from the comparison profiles (acs/acs5/cprofile, acs/acs1/cprofile), which publish none, each estimate has a margin-of-error counterpart at the same code with the E suffix swapped for M — request both to get the margin alongside the estimate. Other dataset families (pep, dec, cbp, ecnbasic, nonemp) publish no margins of error, and an E-final code there is an ordinary code with no M sibling. A code can also name a text column rather than a measure — GEO_ID, on every dataset, is the nationally unique geography identifier and comes back under value with estimate null, which is the code to request when a stable join key is what is wanted."
      },
      "predicates": {
        "type": "object",
        "description": "Filter values keyed by variable code, sent as extra query parameters — e.g. {\"NAICS2017\": \"5112\"} to count only software publishers in cbp. The business datasets (cbp, ecnbasic, nonemp), pep/charv, dec/ddhca, and acs/acs1/spp declare filter dimensions such as industry (NAICS2017/NAICS2022), legal form (LFO), size class (EMPSZES/RCPSZES), tax status (TAXSTAT), operation type (TYPOP), sex (SEX), age (AGE), and population group (POPGROUP). Leaving one unset is not an error: the Census API substitutes its own default, which is the all-categories total on cbp NAICS2017 but a single population group on dec/ddhca POPGROUP and a single sector on ecnbasic NAICS2022 — so an unfiltered value can read like a total without being one. Every unset dimension is named in the response notice and its applied default is echoed per row in applied_filters. Keys are matched case-insensitively, and a blank value is treated as omitted. A value of \"*\" returns one row per category of that dimension for each geography, each row labelled with its category in record (e.g. {\"NAICS2017\": \"*\"} gives King County one row per industry) — a breakdown that can run to over a thousand rows. Code names vary by dataset and vintage — cbp 2023 uses NAICS2017 while nonemp 2023 uses NAICS2022 — so read them from the notice or from census_search_variables. Call census_list_predicate_values for the codes a dimension accepts; NAICS values are standard North American Industry Classification System codes at any depth (51 information, 5112 software publishers).",
        "propertyNames": {
          "type": "string"
        },
        "additionalProperties": {
          "type": "string"
        }
      },
      "tract_fips": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^\\d{6}$",
            "description": "Exactly 6 digits — never padded here, and never \"*\"."
          }
        ],
        "description": "Census tract code scoping the query to one tract (e.g., \"007101\" for Census Tract 71.01), for the levels that sit within a tract — block group on acs/acs5, block group and block on dec/pl. census_resolve_geography returns it as tract_fips, and for a street address also returns the block_group_fips to pass as geography_fips. A tract code is unique only within its county, so it needs parent_fips and a concrete county_fips (not \"*\"). It is exactly 6 digits and is not padded, since \"7101\" and \"71\" do not name one tract. A level that does not sit within a tract rejects it. Blank is treated as omitted."
      },
      "county_fips": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^(\\*|\\d{1,3})$",
            "description": "1 to 3 digits, zero-padded here to the 3 the Census stores, or \"*\"."
          }
        ],
        "description": "County FIPS code when querying tracts or block groups within a specific county (e.g., \"033\" for King County within WA). Required for tract and block-group queries scoped to a county — use alongside parent_fips (state). census_resolve_geography returns this as county_fips. Pass \"*\" to span every county in the state, which is the only way a block-group query reaches a whole state. Blank is treated as omitted."
      },
      "parent_fips": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^(\\*|\\d{1,2})$",
            "description": "1 to 2 digits, zero-padded here to the 2 the Census stores, or \"*\"."
          }
        ],
        "description": "State FIPS code when querying sub-state levels (e.g., \"53\" for Washington). Required for county, tract, and block-group queries. census_resolve_geography returns this as state_fips. Pass \"*\" to span every state. Blank is treated as omitted."
      },
      "geography_fips": {
        "type": "string",
        "description": "FIPS code for the target geography (e.g., \"033\" for a county, \"*\" for every geography at the level within the parent, returned up to limit rows per call and paged with offset). Use census_resolve_geography to obtain this value — it is returned as fips_summary. The Census API matches this literally and its width follows geography_level, so it is passed through unpadded: a county is 3 digits (\"051\", not \"51\") and a tract is 6. parent_fips and county_fips are zero-padded for you; this one is not."
      },
      "geography_level": {
        "type": "string",
        "description": "Level of the target geography (e.g., \"county\", \"tract\", \"state\", \"zip code tabulation area\"). Use census_list_geographies to see valid values for the dataset."
      }
    },
    "additionalProperties": false
  }

  arguments 99 lines copy
• census_resolve_geography reads unknown 13h ago

  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.

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "name"
    ],
    "properties": {
      "name": {
        "type": "string",
        "description": "Place name (e.g., \"King County, WA\", \"Seattle, WA\", \"California\"), 5-digit ZIP code (e.g., \"98109\", resolved to its ZIP Code Tabulation Area), or street address (e.g., \"1600 Pennsylvania Ave NW, Washington, DC 20500\"). Include the state after a comma — its abbreviation or full name, as in \"Chatham County, Georgia\" — to disambiguate places with common names. It narrows a statistical area as well, matching any state the area spans, so \"Kansas City, MO\" and \"Kansas City, KS\" both reach the MO-KS metro area. Matching ignores case, reads \"Saint\" and \"St.\" as the same word, and accepts unaccented spellings (\"Dona Ana County, NM\"). For a statistical area a leading city is enough (\"Denver, CO\" for the Denver-Aurora-Centennial metro area), and an older full name resolves through its leading city (\"Denver-Aurora-Lakewood, CO\")."
      },
      "county_fips": {
        "type": "string",
        "pattern": "^\\d{1,3}$",
        "description": "County FIPS code to resolve within — 1 to 3 digits, zero-padded here to the 3 the Census stores. A tract name is unique only inside its county, so a bare tract name matching two counties comes back as ambiguous_name until this is set: take the countyFips of the candidate you want from that error and re-call. Only county and tract sit within a county, so this restricts resolution to those two levels — pairing it with any other geography_type, or with a street address, is a county_scope_unsupported error rather than a scope quietly dropped. census_query_data takes the same code as its own county_fips but pads nothing, so hand it the 3-digit county_fips returned here, not the shorter value."
      },
      "geography_type": {
        "enum": [
          "state",
          "county",
          "place",
          "tract",
          "metropolitan statistical area/micropolitan statistical area",
          "combined statistical area",
          "consolidated city",
          "zip code tabulation area",
          "economic place"
        ],
        "type": "string",
        "description": "Geography level to resolve to, named exactly as census_query_data's geography_level and census_list_geographies name it. Auto-detection covers only state, county, place, tract, and zip code tabulation area: zip code tabulation area for a 5-digit ZIP or ZIP+4 — the ACS's ZIP-shaped area, not cbp's \"zip code\" level, which takes the ZIP itself with no resolution — state for a two-letter abbreviation or a spelled-out state name, county when the name contains the word \"County\" or \"Parish\", county then place for the word \"Borough\" (an Alaska borough is a county, a PA or NJ borough a place), tract for the word \"Tract\", otherwise place (incorporated places and census-designated places together) with a fallback to county, where a census-designated place answers only when no incorporated place or county has the exact name — \"Arlington, VA\" is Arlington County, and set \"place\" to reach the Arlington CDP. The other four are never auto-detected and must be set explicitly, because their names overlap city names — \"metropolitan statistical area/micropolitan statistical area\" covers both metro and micro areas and yields a 5-digit code, \"combined statistical area\" yields a 3-digit code, \"consolidated city\" covers the eight merged city-county governments (Nashville-Davidson, Louisville/Jefferson County, Indianapolis, Athens-Clarke County, Augusta-Richmond County, Butte-Silver Bow, Milford CT, Greeley County KS), and \"economic place\" yields the 8-digit code ecnbasic 2022 publishes a place under: the 3-digit county it lies in (000 when it spans counties) followed by its 5-digit place code. Economic places are the incorporated places, census-designated places, and county subdivisions the 2022 Economic Census tabulates, plus each county's remainder (\"Balance of Adams County, WA\"). Setting it explicitly also overrides auto-detection — \"New York\" auto-detects as the state, so New York City needs \"place\"."
      }
    },
    "additionalProperties": false
  }

  arguments 34 lines copy
• census_compare_geographies reads unknown 11h ago

  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 within one industry, size class, or population group — a comparison that omits one ranks on a default the Census API picks, which is an all-categories total on some dimensions and a single category on others. Each row names the defaults that were applied in applied_filters, and census_list_predicate_values enumerates the codes a dimension accepts. A dataset that publishes several records per geography cannot be ranked until one is pinned: pep/charv publishes an April estimates base and a July estimate, so a comparison that pins neither fails with ambiguous_rows rather than giving every geography two ranks — pass predicates {"MONTH": "7"} for the July estimate.

  mcp-tool

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "variables",
      "geography_level"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year (default: latest available for the dataset)."
      },
      "limit": {
        "type": "integer",
        "maximum": 500,
        "minimum": 1,
        "description": "Maximum geographies to return (default: 50, max: 500). When results are truncated, totalCount says how many matched."
      },
      "within": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^(\\*|\\d{1,2})$",
            "description": "1 to 2 digits, zero-padded here to the 2 the Census stores, or \"*\"."
          }
        ],
        "description": "State FIPS to constrain results (e.g., \"53\" to compare counties or tracts within WA only). Omit to compare all geographies at the level nationally. Use census_resolve_geography to get state_fips. Pass \"*\" to span every state. Blank is treated as omitted."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset to query (default: \"acs/acs5\"). Use census_list_datasets for valid values. Case is ignored, and a two-part code can be given by its last part alone — \"acs5\" is acs/acs5, \"pl\" is dec/pl. Three-part codes such as acs/acs5/profile must be given in full. The response echoes the resolved code."
      },
      "sort_by": {
        "type": "string",
        "description": "Variable code to rank on (default: the first code in variables), uppercased like the variables. Must be one of the requested codes, or the call fails with sort_by_not_requested. Geographies rank on that code's own value, so a count ranks by size and only a published percentage such as S1701_C03_001E or DP03_0128PE ranks by rate."
      },
      "sort_dir": {
        "enum": [
          "asc",
          "desc"
        ],
        "type": "string",
        "description": "Sort direction (default: \"desc\" — highest value first)."
      },
      "variables": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Variable codes to compare (e.g., [\"B19013_001E\", \"B19013_001M\"]); the ranking is on one of them, set by sort_by. Codes are uppercased before the request, and each row is keyed by the uppercase code. At most 49 per call: the Census API accepts 50 columns per request and every query also sends NAME. On datasets where a label column is added for each filter dimension left unset, or record columns are added (cbp, ecnbasic, nonemp, pep/charv, dec/ddhca, acs/acs1/spp), the maximum is lower, and too_many_variables states the exact number for the comparison. On ACS datasets, add the margin-of-error counterpart of a code (same code, E suffix swapped for M) for reliability context. The ACS comparison profiles (acs/acs5/cprofile, acs/acs1/cprofile) and the other dataset families (pep, dec, cbp, ecnbasic, nonemp) publish no margins of error."
      },
      "predicates": {
        "type": "object",
        "description": "Filter values keyed by variable code, applied to every geography in the comparison — e.g. {\"NAICS2017\": \"5112\"} to rank counties by their software-publisher establishment count in cbp. The business datasets (cbp, ecnbasic, nonemp), pep/charv, dec/ddhca, and acs/acs1/spp declare filter dimensions such as industry (NAICS2017/NAICS2022), legal form (LFO), size class (EMPSZES/RCPSZES), tax status (TAXSTAT), operation type (TYPOP), sex (SEX), age (AGE), and population group (POPGROUP). Leaving one unset is not an error: the Census API substitutes its own default, which is the all-categories total on cbp NAICS2017 but a single population group on dec/ddhca POPGROUP and a single sector on ecnbasic NAICS2022 — so a ranking can read like an overall one without being it. Every unset dimension is named in the response notice and its applied default is echoed per row in applied_filters. Keys are matched case-insensitively, and a blank value is treated as omitted. A value of \"*\" returns every geography once per category of that dimension, which a ranking cannot hold, so it fails with ambiguous_rows naming the dimension to pin — use census_query_data for a per-category breakdown. Code names vary by dataset and vintage — cbp 2023 uses NAICS2017 while nonemp 2023 uses NAICS2022 — so read them from the notice or from census_search_variables. Call census_list_predicate_values for the codes a dimension accepts; NAICS values are standard North American Industry Classification System codes at any depth (51 information, 5112 software publishers).",
        "propertyNames": {
          "type": "string"
        },
        "additionalProperties": {
          "type": "string"
        }
      },
      "geographies": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Optional list of specific geographies to include; only these are returned. Prefer full GEOIDs — the level concatenated with its parents, e.g. \"53033\" for King County WA and \"06037\" for Los Angeles County CA — which are nationally unique and so work across states. Bare level codes (\"033\") are also accepted but match that code in every state unless within scopes them to one. A GEOID is easiest taken from the geography_geoid field of a census_query_data or census_compare_geographies row; from census_resolve_geography, concatenate state_fips, then county_fips when it is present, then fips_summary. Entries that match nothing, and bare codes that match more than one state, are named in the response notice."
      },
      "within_county": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^(\\*|\\d{1,3})$",
            "description": "1 to 3 digits, zero-padded here to the 3 the Census stores, or \"*\"."
          }
        ],
        "description": "County FIPS to constrain tract or block-group comparisons to a single county within the state specified by within (e.g., \"033\" for King County). Required when geography_level is \"tract\" or \"block group\" and you want county-scoped results. census_resolve_geography returns this as county_fips. Pass \"*\" to span every county in the state, which is the only way a block-group comparison reaches a whole state. Blank is treated as omitted."
      },
      "geography_level": {
        "type": "string",
        "description": "The level to compare across (e.g., \"state\", \"county\", \"tract\"). Use census_list_geographies to see valid values for the dataset."
      }
    },
    "additionalProperties": false
  }

  arguments 93 lines copy
• census_get_variable reads unknown 11h ago

  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_predicate_values.

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "variables"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year (default: latest available for the dataset)."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset the variables belong to (default: \"acs/acs5\"). Use census_list_datasets to discover valid values. Case is ignored, and a two-part code can be given by its last part alone — \"acs5\" is acs/acs5, \"pl\" is dec/pl. Three-part codes such as acs/acs5/profile must be given in full. The response echoes the resolved code."
      },
      "variables": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "One or more variable codes to look up (e.g., [\"B19013_001E\", \"B19013_001M\"]). Codes are trimmed and matched regardless of case, and the response echoes the dataset's own spelling — uppercase everywhere except the comparison profiles' significance columns (e.g., CP03_2024to2019_062SS)."
      }
    },
    "additionalProperties": false
  }

  arguments 25 lines copy
• census_list_datasets reads unknown never probed

  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.

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "properties": {
      "filter": {
        "type": "string",
        "description": "Keyword to filter datasets by name or description. Omit to list all datasets."
      }
    },
    "additionalProperties": false
  }

  arguments 11 lines copy
• census_search_variables reads unknown 11h ago

  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.

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "query"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year to search (default: latest available for the dataset)."
      },
      "limit": {
        "type": "integer",
        "maximum": 100,
        "minimum": 1,
        "description": "Maximum results to return (default: 20, max: 100). Increase if totalMatches greatly exceeds the limit."
      },
      "query": {
        "type": "string",
        "description": "Keywords to search (e.g., \"median household income\", \"poverty\", \"bachelor's degree\"). Each word must match a whole word of the label or of the concept, ignoring case and punctuation, so \"rate\" does not match \"separated\". A column shared across tables, such as GEO_ID, is matched on its label only, and a margin of error on its estimate's label. When no variable contains every word, the results are the variables containing the most words, and the notice says how many that was."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset to search within (default: \"acs/acs5\"). Use census_list_datasets to discover options. Case is ignored, and a two-part code can be given by its last part alone — \"acs5\" is acs/acs5, \"pl\" is dec/pl. Three-part codes such as acs/acs5/profile must be given in full. The response echoes the resolved code, and the default year is that dataset's latest."
      }
    },
    "additionalProperties": false
  }

  arguments 28 lines copy
• census_list_predicate_values reads unknown 13h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "predicate",
      "dataset"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year (default: latest available for the dataset)."
      },
      "limit": {
        "type": "integer",
        "maximum": 500,
        "minimum": 1,
        "description": "Maximum codes to return (default: 50, max: 500). totalCount says how many matched."
      },
      "query": {
        "type": "string",
        "description": "Keyword to narrow the list, matched case-insensitively against each code and label (e.g., \"software\" against NAICS2017, \"exempt\" against TAXSTAT). Omit to list from the start. NAICS and POPGROUP run to thousands of codes, so a keyword is the practical way to use them."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset the dimension belongs to (e.g., \"cbp\", \"nonemp\", \"ecnbasic\", \"dec/ddhca\", \"pep/charv\", \"acs/acs1/spp\"). Use census_list_datasets to discover valid values. Case is ignored, and a two-part code can be given by its last part alone — \"ddhca\" is dec/ddhca, \"charv\" is pep/charv. Three-part codes such as acs/acs1/spp must be given in full. The response echoes the resolved code. Dimension codes are vintage-specific, so the dataset and year must match the query the values are for."
      },
      "predicate": {
        "type": "string",
        "description": "Filter dimension code to enumerate (e.g., \"EMPSZES\", \"LFO\", \"POPGROUP\", \"NAICS2017\"). Trimmed and uppercased, and the response echoes that spelling. The response notice of census_query_data names the dimensions a dataset declares, and census_search_variables finds them by keyword."
      },
      "within_naics": {
        "anyOf": [
          {
            "type": "string",
            "const": ""
          },
          {
            "type": "string",
            "pattern": "^\\d{2,8}(-\\d{2})?$",
            "description": "A NAICS code: 2 to 8 digits, or a hyphenated sector range such as \"31-33\"."
          }
        ],
        "description": "Industry code to scope the enumeration by, for dimensions the Census publishes per industry. On ecnbasic, TAXSTAT and TYPOP return only the all-establishments row until a NAICS sector is named — pass a sector code such as \"62\" (Health Care) or \"42\" (Wholesale Trade) and the result is complete for that industry alone. Ignored for dimensions with a published value list. Get sector codes by calling this tool on the dataset's own NAICS dimension. Blank is treated as omitted."
      }
    },
    "additionalProperties": false
  }

  arguments 47 lines copy
• census_list_geographies reads unknown 9h ago

  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.

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  {
    "type": "object",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "required": [
      "dataset"
    ],
    "properties": {
      "year": {
        "type": "number",
        "description": "Vintage year. Defaults to the latest available year for the dataset."
      },
      "dataset": {
        "type": "string",
        "description": "Dataset code (e.g., \"acs/acs5\", \"acs/acs1\"). Use census_list_datasets to discover valid values. Case is ignored, and a two-part code can be given by its last part alone — \"acs5\" is acs/acs5, \"pl\" is dec/pl. Three-part codes such as acs/acs5/profile must be given in full. The response echoes the resolved code."
      }
    },
    "additionalProperties": false
  }

  arguments 18 lines copy

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