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Georgia Civic Data

Georgia education, Census, and immigration data: query, filter, aggregate, and link datasets.

Connector by georgiacivicdata.orgOfficial · Domain verified by the MCP Registry

IndexedLicense unknownRemote (HTTP)
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Install

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

npx agentmarket install connector/georgiacivicdata-org-georgia-civic-data

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Remote endpoint

  • https://mcp.georgiacivicdata.org/mcp/ Remote (HTTP)

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About

Georgia education, Census, and immigration data: query, filter, aggregate, and link datasets.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools
12 read · 0 write · 0 delete · 1 not yet classified
Secrets
None asked for
Install scripts
Nothing to install (hosted)
Runs as
Hosted (remote endpoint)

Tools by what they do 13

From the running server's own tool list (tools/list) on Oct 25, 2026.

Read 12 · Look things up without changing anything.

  • aggregate
  • describe_dataset
  • describe_dimension
  • distinct_values
  • get_contract
  • get_dimension
  • link_tables
  • list_datasets
  • lookup_school_zone
  • query_dataset
  • resolve_entity
  • search_datasets

Not yet classified 1 · We could not tell from the name and description.

  • link_query

Network hosts it talks to 1

  • mcp.georgiacivicdata.org · found in remote endpoint, install command

Tools (13)

As the server listed them on Oct 25, 2026. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • aggregate

    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…

  • describe_dataset

    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` (numerato…

  • describe_dimension

    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.

  • distinct_values

    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 …

  • get_contract

    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.

  • get_dimension

    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.

  • link_query

    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 lin…

  • link_tables

    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',…

  • list_datasets

    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. A top-l…

  • lookup_school_zone

    Which Georgia public school attendance zones (elementary, middle, high) contain a place, or (attendance_rules=true) how attendance zones work in Georgia. Give exactly one of: lat + lon; an address (geocoded by the U.S. Census Geocoder); a 2020 census tract_geoid (returns every zone covering the tract with the share of its 2020 population in each, or, when none does, a coverage_reason saying why); or attendance_rules=true with no place. Optional grade (pk, k, 01-12) and year. Returns the district, the zoned schools with labels and provenance (method, accuracy_class, positional_rmse_m, boundary_…

  • query_dataset

    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 col…

  • resolve_entity

    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 pl…

  • search_datasets

    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).

Signals

Last release
Sep 7, 2026 (1 month ago)
Repository
Not found on GitHub (deleted or private)

Details

Registry name
org.georgiacivicdata/georgia-civic-data
Version
0.1.0
Auth
Auth not stated
License
License unknown
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Georgia Civic Data MCP server. Agent Mart. https://agentmart.dev/connectors/georgiacivicdata-org-georgia-civic-data (last updated 2026-10-24)