_ registry / mcp http-sse · checked 16h ago

Georgia Public Data

https://mcp.georgiacivicdata.org

Registry code: 1120f92dafdaac2b

api record

Cleaned, standardized Georgia public data (education, Census context, and immigration) as a star schema — lean fact tables (keys + metrics) joined to shared dimensions (district / school / county / demographic labels), described by ODCS data contracts and served over DuckDB.

Scope: tabular and structured data only; this server holds no documents. For Georgia government documents (current-session General Assembly bills, recent Supreme Court of Georgia opinions, and the Official Code of Georgia Annotated), use Georgia Commons (https://georgiacommons.org).

endpoint
https://mcp.georgiacivicdata.org/mcp/
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
413ms

last good check

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of 12 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

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_ what it can do 12 tools
12 never probed 0 of 12 classified

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  • aggregate unknown never probed

    Compute a grouped aggregate over one topic — the aggregation-first path. `agg` is one of avg/sum/min/max/count/weighted_rate; `metric` is a metric column (DEFAULTS to the topic key_metric; ignored for count); `group_by` is a list of grain columns (year, FK codes like district_code or county_fips, or categoricals — see describe_dataset). `weighted_rate` computes a true population-weighted SUM(numerator)/SUM(denominator) for a rate key metric (when the contract declares the components) — prefer it over `avg` for a rate across multiple places/years, since `avg` means the per-row rates and ignores population. Supports the same `filters` / `year` / `year_min`-`year_max` / `detail` as query_dataset, plus `order_by`+`order` for top-N (order_by 'value' for the aggregated column; NULL cells sort LAST in either direction). Returns one small row per group with `<metric>_<agg>` (or `row_count`) plus coverage diagnostics (input_rows / non-null counts) so suppression is visible; `aggregation_scope` flags whether rows are source-published at this grain or recomputed from a finer detail (prefer source-published — see the advisory). A `sum`/`weighted_rate` ACROSS a column the contract declares non_additive (overlapping rows, e.g. county rows that count one application in several counties) still returns, but carries a top-level `non_additive` block and a leading `non_additive_sum` advisory: that figure is NOT a total — group_by the column or use the published total row instead. Aggregates SKIP NULLs and NULL means SUPPRESSED not zero. No raw SQL: all identifiers are contract-allowlisted.

    mcp-tool

    {
  "type": "object",
  "title": "aggregateArguments",
  "required": [
    "topic"
  ],
  "properties": {
    "agg": {
      "type": "string",
      "title": "Agg",
      "default": "avg"
    },
    "year": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year",
      "default": null
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Limit",
      "default": null
    },
    "order": {
      "type": "string",
      "title": "Order",
      "default": "asc"
    },
    "topic": {
      "type": "string",
      "title": "Topic"
    },
    "detail": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Detail",
      "default": null
    },
    "metric": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Metric",
      "default": null
    },
    "offset": {
      "type": "integer",
      "title": "Offset",
      "default": 0
    },
    "filters": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "title": "Filters",
      "default": null
    },
    "group_by": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "null"
        }
      ],
      "title": "Group By",
      "default": null
    },
    "order_by": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Order By",
      "default": null
    },
    "year_max": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year Max",
      "default": null
    },
    "year_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year Min",
      "default": null
    },
    "main_topic": {
      "type": "string",
      "title": "Main Topic",
      "default": "education"
    }
  }
}
    arguments 145 lines
  • describe_dimension unknown never probed

    Schema for one dimension (districts / schools / counties / demographics): the (possibly composite) primary key, the attribute columns a join attaches, the cross-dataset `link_keys` (e.g. districts.district_census_id → Census via the crosswalk — a 5-digit school-district code, NOT a county FIPS), and demographics `semantics` (within a category the values are mutually exclusive; `all` is the denominator). Read this before writing a link_query join.

    mcp-tool

    {
  "type": "object",
  "title": "describe_dimensionArguments",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "title": "Name"
    }
  }
}
    arguments 13 lines
  • get_dimension unknown never probed

    Paginated read of a dimension table — the label lookups (district names, school names, county names, demographic labels). Rows are ordered by the dimension's primary key so paging is stable. Use describe_dimension for the schema and link keys. Small page defaults; `truncated` + a `bulk_export` pointer signal when to pull the full table elsewhere.

    mcp-tool

    {
  "type": "object",
  "title": "get_dimensionArguments",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "title": "Name"
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Limit",
      "default": null
    },
    "offset": {
      "type": "integer",
      "title": "Offset",
      "default": 0
    }
  }
}
    arguments 30 lines
  • get_contract unknown never probed

    Return the authoritative ODCS v3.2 data contract for a topic (kind='topic') or a dimension (kind='dimension') so you can consume the machine-readable schema without cloning the repo. fmt='yaml' (default) returns the document verbatim as text; fmt='json' returns it parsed. Only approved topics and loaded dimensions expose a contract.

    mcp-tool

    {
  "type": "object",
  "title": "get_contractArguments",
  "required": [
    "name"
  ],
  "properties": {
    "fmt": {
      "type": "string",
      "title": "Fmt",
      "default": "yaml"
    },
    "kind": {
      "type": "string",
      "title": "Kind",
      "default": "topic"
    },
    "name": {
      "type": "string",
      "title": "Name"
    },
    "main_topic": {
      "type": "string",
      "title": "Main Topic",
      "default": "education"
    }
  }
}
    arguments 28 lines
  • link_tables unknown never probed

    List the tables link_query can read (curated gold paths only) and the join keys that bridge facts → dimensions → Census geography. Call this BEFORE writing a link_query. Two-tier to stay context-cheap: with NO arguments it returns a LEAN index — every table's name, grain, detail levels, default `read_parquet(...)` snippet, and join keys (enough to pick tables and write a single-detail join). To get every column and a snippet per detail level for the few tables you actually need, call again with `tables=["<name>", ...]` (a `name` from the index, e.g. 'education/gosa/attendance' or 'attendance', or a dimension like 'districts'). Paste the `read_parquet(...)` snippets verbatim into your SQL — they are exactly what the sandbox accepts.

    mcp-tool

    {
  "type": "object",
  "title": "link_tablesArguments",
  "properties": {
    "tables": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "null"
        }
      ],
      "title": "Tables",
      "default": null
    }
  }
}
    arguments 21 lines
  • link_query unknown never probed

    Run a cross-dataset / cross-topic analytical SQL query that the per-topic query_dataset filters can't express — e.g. join education, Census, or immigration facts to a dimension (or another dataset) on shared geography (immigration and Census county topics share county_fips directly). READ-ONLY, SANDBOXED DuckDB: one SELECT (or WITH … SELECT); no DDL/DML/COPY/ATTACH/INSTALL/PRAGMA/SET/CALL; you may only read_parquet() the curated gold paths returned by link_tables (call it first and paste the snippets) — querying a file path directly is rejected. Joins use the keys from describe_dimension's link_keys (districts.district_census_id bridges to Census via the crosswalk — it is a school-district code, not a county FIPS, so a district is not 1:1 with a county). Results are row- and byte-capped and time-limited; `truncated` flags when capped — add aggregation or a tighter WHERE rather than dumping rows. NULL means suppressed, not zero. On a violation you get a self-describing error naming the offending token/path.

    mcp-tool

    {
  "type": "object",
  "title": "link_queryArguments",
  "required": [
    "sql"
  ],
  "properties": {
    "sql": {
      "type": "string",
      "title": "Sql"
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Limit",
      "default": null
    }
  }
}
    arguments 25 lines
  • list_datasets unknown never probed

    Enumerate every approved Georgia dataset (topic) and the shared dimensions. Each topic entry is a LEAN summary — name/keys, year coverage (year_min/year_max + year_gaps), detail levels + default detail, a has_demographic flag (false = no demographic axis, so there is no all-students demographic row to filter), tags, contract version, and a one-line description — enough to pick a topic; call describe_dataset for its full schema (columns, filters, grain, source, example queries). Each dimension entry carries its primary key, attribute columns, and (for districts) cross-dataset link keys. Call this first to learn what exists — but for a NAMED task (you already know roughly the topic), prefer search_datasets, which returns far fewer bytes than this full catalog.

    mcp-tool

    {
  "type": "object",
  "title": "list_datasetsArguments",
  "properties": {}
}
    arguments 5 lines
  • search_datasets unknown never probed

    Keyword search over the catalog metadata (topic names, descriptions, tags, AND column names/descriptions) — the discovery entry point when you don't know the exact topic name. Returns lean topic summaries per hit with a relevance score and which fields matched, plus a `dimension_matches` list when the query also hits a dimension (e.g. 'district'). Most acronyms work; the short ones `ap`/`el`/`ib` are recognized. Follow up with describe_dataset. `limit` caps results (default 20, max 100).

    mcp-tool

    {
  "type": "object",
  "title": "search_datasetsArguments",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 20
    },
    "query": {
      "type": "string",
      "title": "Query"
    }
  }
}
    arguments 18 lines
  • query_dataset unknown never probed

    Query one topic's gold facts with dimension labels joined in (the district/school/county/demographic names come back on every row). `filters` is a dict of column → value or list-of-values: FK codes (district_code, school_code, county_fips, demographic) and any categorical column — see describe_dataset's `filters` for the exact keys and enum values. Use `year` (exact) OR `year_min`/`year_max` (range), never both. `detail` picks the grain (default is the finest available). Returns `rows` plus a `columns` descriptor array (type/role/unit/null-meaning, and `is_key_metric` flagging the headline column) so you interpret values and NULLs correctly — NULL usually means SUPPRESSED, not zero (see null_semantics). The top-level `key_metric` echoes which column is the answer. Use `columns` to project a subset, `include_labels=false` to skip the joined name columns (codes only), and `order_by`+`order` for server-side top-N instead of over-fetching. Pages are small (default 100, max 500); when `truncated` is true a `bulk_export` block points at the REST CSV/Parquet endpoint and the source path for the full pull — do not loop pagination to dump a table. A bad filter returns a self-describing error listing the valid keys/values.

    mcp-tool

    {
  "type": "object",
  "title": "query_datasetArguments",
  "required": [
    "topic"
  ],
  "properties": {
    "year": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year",
      "default": null,
      "description": "Exact year. Use this OR year_min/max."
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer",
          "minimum": 1
        },
        {
          "type": "null"
        }
      ],
      "title": "Limit",
      "default": null,
      "description": "Page size (default 100, max 500)."
    },
    "order": {
      "type": "string",
      "title": "Order",
      "default": "asc",
      "description": "Sort direction for order_by: 'asc' or 'desc'."
    },
    "topic": {
      "type": "string",
      "title": "Topic",
      "description": "Topic name, e.g. 'act_scores'."
    },
    "detail": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Detail",
      "default": null,
      "description": "Grain (e.g. schools/districts/states); default finest."
    },
    "offset": {
      "type": "integer",
      "title": "Offset",
      "default": 0,
      "minimum": 0,
      "description": "Row offset for paging (>= 0)."
    },
    "columns": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "null"
        }
      ],
      "title": "Columns",
      "default": null,
      "description": "Project only these output columns (fact columns + joined label columns). Smaller pages / fewer column reads. Omit for all columns."
    },
    "filters": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "title": "Filters",
      "default": null,
      "description": "Column → value (or list of values) filters. Keys are FK columns (district_code, school_code, county_fips, demographic) and categorical columns; read describe_dataset's `filters` for the exact keys and enum values FIRST. A value list is a union (OR); multiple keys AND together. A wrong key/value returns a self-describing error listing the valid ones."
    },
    "order_by": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Order By",
      "default": null,
      "description": "Order by one fact column or joined label column (for server-side top-N). Default order is the row grain. NULL (suppressed) cells sort LAST in either direction, so a metric top-N is never polluted by suppressed rows."
    },
    "year_max": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year Max",
      "default": null,
      "description": "Inclusive upper year bound (range)."
    },
    "year_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Year Min",
      "default": null,
      "description": "Inclusive lower year bound (range)."
    },
    "main_topic": {
      "type": "string",
      "title": "Main Topic",
      "default": "education",
      "description": "Main topic: 'education', 'census', or 'immigration'."
    },
    "include_labels": {
      "type": "boolean",
      "title": "Include Labels",
      "default": true,
      "description": "Join district/school/county/demographic name columns (default true); false = codes only (faster, leaner)."
    }
  }
}
    arguments 148 lines
  • distinct_values unknown never probed

    List the distinct values of ONE filterable column of a topic — the fast way to learn valid filter values before query_dataset, especially for FREE categoricals and FK codes (district_code/school_code/county_fips/demographic) that carry no enum in describe_dataset (a wrong guess otherwise returns an empty page with no error). `column` must be a filterable column (see describe_dataset's `filters`). Optional `prefix` does a case-insensitive starts-with filter; `limit` caps results (default 50). Enum-bearing columns return their contract enum directly; others run a capped SELECT DISTINCT over the gold data. `truncated` flags when the list is capped.

    mcp-tool

    {
  "type": "object",
  "title": "distinct_valuesArguments",
  "required": [
    "topic",
    "column"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 50
    },
    "topic": {
      "type": "string",
      "title": "Topic"
    },
    "column": {
      "type": "string",
      "title": "Column"
    },
    "detail": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Detail",
      "default": null
    },
    "prefix": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Prefix",
      "default": null
    },
    "main_topic": {
      "type": "string",
      "title": "Main Topic",
      "default": "education"
    }
  }
}
    arguments 52 lines
  • resolve_entity unknown never probed

    Resolve a place or demographic NAME or CODE to its stable keys + labels — the right way to turn 'Atlanta Public Schools' / 'Fulton' / a raw code into the district_code / school_code / county_fips / demographic to filter by (a wrong code guess otherwise returns an empty query_dataset page). `kind` is district / school / county / demographic ('Fulton' as kind='county' → the county; as kind='district' → the school district — they are different things). Fuzzy-matches and ranks candidates, flags `ambiguous` when several tie, and reads only the small dimension table (no fact scan). A real Georgia place is never matched to a different one: a consolidated city returns its county (matched_on=consolidated_city: 'Columbus' -> Muscogee), another city asked as a county returns the county it lies in flagged ambiguous, and a one-letter misspelling returns matched_on=typo. Read `place_note` and tell the user when it is present.

    mcp-tool

    {
  "type": "object",
  "title": "resolve_entityArguments",
  "required": [
    "kind",
    "query"
  ],
  "properties": {
    "kind": {
      "type": "string",
      "title": "Kind"
    },
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 10
    },
    "query": {
      "type": "string",
      "title": "Query"
    }
  }
}
    arguments 23 lines
  • describe_dataset unknown never probed

    Full schema for one topic: every column (name/type/role/unit/value range/null-meaning), the exact `filters` list with enum values (read this before query_dataset — it is the authoritative set of filter keys), the FK→dimension join shape (`foreign_keys`), example queries, usage, limitations, null semantics, tags, and `schema_hash` (for cache/drift detection). The top-level `key_metric` names the single headline column most answers want; each column carries `key_metric_grain_contributor` (a grain axis the key metric is only comparable within — pin or group by it) and `metric_component` (numerator/denominator of a rate/average metric). `recommended_query` gives the safe default query shape (key metric + filters to pin + required single-selects) plus a `ranking` recipe for top/bottom-N asks; `filter_hints` lists paired filters; `non_additive` lists columns whose rows overlap (never sum across them; a metric entry names metric columns never to add together) and `value_implications` values that imply another column's value; each categorical filter carries `has_total` / `requires_single_value`. Pass `verbosity='schema'` for a much smaller payload that drops the prose (description/usage/limitations/example queries/column descriptions) but keeps every field needed to compose a correct query — use it when you only need the filter keys and enums; prefer the default 'full' before reporting conclusions (the limitations prose carries the caveats). On an unknown topic returns a self-describing error listing available topics + a 'did you mean' hint. `main_topic` defaults to 'education'; pass 'census' for Census topics or 'immigration' for immigration topics.

    mcp-tool

    {
  "type": "object",
  "title": "describe_datasetArguments",
  "required": [
    "topic"
  ],
  "properties": {
    "topic": {
      "type": "string",
      "title": "Topic"
    },
    "verbosity": {
      "type": "string",
      "title": "Verbosity",
      "default": "full",
      "description": "'full' (default) or 'schema' (drops prose; keeps columns/filters/enums/key_metric/recommended_query)."
    },
    "main_topic": {
      "type": "string",
      "title": "Main Topic",
      "default": "education"
    }
  }
}
    arguments 24 lines
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