{"name":"org.georgiacivicdata/georgia-civic-data","slug":"georgiacivicdata-georgia-civic-data","title":"Georgia Civic Data","description":"Georgia education, Census, and immigration data: query, filter, aggregate, and link datasets.","url":"https://mcp.market/server/georgiacivicdata-georgia-civic-data","rating":null,"grade":"C","score":60,"certified":false,"status":"active","category":"other","tags":[],"presence":{"score":8,"stars":null,"forks":null,"downloads_week":null,"last_push_at":null,"license":null},"uptime":{"percent":100,"checks":6,"ok":6,"last_checked_at":"2026-09-21T00:40:47.139Z","last_ok_at":"2026-09-21T00:40:47.139Z","latency_ms":533},"claimed":false,"transport":"remote","callable_via_gateway":true,"default_price_micros":0,"repository":"https://github.com/shanejorr/georgia-data","website":"https://georgiacivicdata.org/mcp","version":"0.1.0","remotes":[{"type":"streamable-http","url":"https://mcp.georgiacivicdata.org/mcp/"}],"packages":[],"tools":[{"name":"aggregate","description":"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). Aggregates SKIP NULLs and NULL means SUPPRESSED not zero. No raw SQL: all identifiers are contract-allowlisted.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"topic":{"title":"Topic","type":"string"},"metric":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Metric"},"agg":{"default":"avg","title":"Agg","type":"string"},"group_by":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Group By"},"filters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"default":null,"title":"Filters"},"year":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Year"},"year_min":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Year Min"},"year_max":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Year Max"},"detail":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Detail"},"limit":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Limit"},"offset":{"default":0,"title":"Offset","type":"integer"},"order_by":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Order By"},"order":{"default":"asc","title":"Order","type":"string"},"main_topic":{"default":"education","title":"Main Topic","type":"string"}},"required":["topic"],"title":"aggregateArguments"}},{"name":"describe_dataset","description":"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; 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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"topic":{"title":"Topic","type":"string"},"main_topic":{"default":"education","title":"Main Topic","type":"string"},"verbosity":{"default":"full","description":"'full' (default) or 'schema' (drops prose; keeps columns/filters/enums/key_metric/recommended_query).","title":"Verbosity","type":"string"}},"required":["topic"],"title":"describe_datasetArguments"}},{"name":"describe_dimension","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"describe_dimensionArguments"}},{"name":"distinct_values","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"topic":{"title":"Topic","type":"string"},"column":{"title":"Column","type":"string"},"prefix":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Prefix"},"limit":{"default":50,"title":"Limit","type":"integer"},"main_topic":{"default":"education","title":"Main Topic","type":"string"},"detail":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"title":"Detail"}},"required":["topic","column"],"title":"distinct_valuesArguments"}},{"name":"get_contract","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"name":{"title":"Name","type":"string"},"main_topic":{"default":"education","title":"Main Topic","type":"string"},"kind":{"default":"topic","title":"Kind","type":"string"},"fmt":{"default":"yaml","title":"Fmt","type":"string"}},"required":["name"],"title":"get_contractArguments"}},{"name":"get_dimension","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"name":{"title":"Name","type":"string"},"limit":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Limit"},"offset":{"default":0,"title":"Offset","type":"integer"}},"required":["name"],"title":"get_dimensionArguments"}},{"name":"link_query","description":"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.","write_action":true,"price_micros":0,"input_schema":{"type":"object","properties":{"sql":{"title":"Sql","type":"string"},"limit":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"title":"Limit"}},"required":["sql"],"title":"link_queryArguments"}},{"name":"link_tables","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"tables":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"title":"Tables"}},"title":"link_tablesArguments"}},{"name":"list_datasets","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{},"title":"list_datasetsArguments"}},{"name":"query_dataset","description":"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.","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"topic":{"description":"Topic name, e.g. 'act_scores'.","title":"Topic","type":"string"},"main_topic":{"default":"education","description":"Main topic: 'education', 'census', or 'immigration'.","title":"Main Topic","type":"string"},"filters":{"anyOf":[{"additionalProperties":true,"type":"object"},{"type":"null"}],"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.","title":"Filters"},"year":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"Exact year. Use this OR year_min/max.","title":"Year"},"year_min":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"Inclusive lower year bound (range).","title":"Year Min"},"year_max":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"Inclusive upper year bound (range).","title":"Year Max"},"detail":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"Grain (e.g. schools/districts/states); default finest.","title":"Detail"},"limit":{"anyOf":[{"minimum":1,"type":"integer"},{"type":"null"}],"default":null,"description":"Page size (default 100, max 500).","title":"Limit"},"offset":{"default":0,"description":"Row offset for paging (>= 0).","minimum":0,"title":"Offset","type":"integer"},"columns":{"anyOf":[{"items":{"type":"string"},"type":"array"},{"type":"null"}],"default":null,"description":"Project only these output columns (fact columns + joined label columns). Smaller pages / fewer column reads. Omit for all columns.","title":"Columns"},"include_labels":{"default":true,"description":"Join district/school/county/demographic name columns (default true); false = codes only (faster, leaner).","title":"Include Labels","type":"boolean"},"order_by":{"anyOf":[{"type":"string"},{"type":"null"}],"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.","title":"Order By"},"order":{"default":"asc","description":"Sort direction for order_by: 'asc' or 'desc'.","title":"Order","type":"string"}},"required":["topic"],"title":"query_datasetArguments"}},{"name":"resolve_entity","description":"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).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"kind":{"title":"Kind","type":"string"},"query":{"title":"Query","type":"string"},"limit":{"default":10,"title":"Limit","type":"integer"}},"required":["kind","query"],"title":"resolve_entityArguments"}},{"name":"search_datasets","description":"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).","write_action":false,"price_micros":0,"input_schema":{"type":"object","properties":{"query":{"title":"Query","type":"string"},"limit":{"default":20,"title":"Limit","type":"integer"}},"required":["query"],"title":"search_datasetsArguments"}}],"scan":{"score":60,"grade":"C","scanned_at":"2026-09-20T00:27:24.906Z","report":{"scannerVersion":"0.1.9","scannedAt":"2026-09-20T00:27:24.922Z","components":{"code":{"score":-1,"max":25,"notes":["remote-only server, no package to scan"]},"reliability":{"score":20,"max":20,"notes":["remote reachable in 755ms"]},"poisoning":{"score":15,"max":15,"notes":["12 tool descriptions checked"]},"auth":{"score":3,"max":15,"notes":["open endpoint exposes 1 write-action tools with no auth"]},"maintenance":{"score":3,"max":15,"notes":["repository not readable: repo not found"]},"identity":{"score":4,"max":10,"notes":["verified namespace with website, no repo"]}},"findings":[{"id":"auth.open-write","severity":"high","component":"auth","title":"Write-action tools reachable without authentication"}],"inputs":{"probes":[{"url":"https://mcp.georgiacivicdata.org/mcp/","reachable":true,"authRequired":false,"latencyMs":755,"serverInfo":{"name":"Georgia Public Data","version":"1.27.2"}}],"packages":[],"repo":{"found":false,"owner":"shanejorr","repo":"georgia-data","error":"repo not found"},"icon":{"url":"https://georgiacivicdata.org/icon.svg?icon.0xt06npkdu_p7.svg?dpl=dpl_EeM5gZESoUQD2HQkjwEzAuJ9NumN","source":"site"},"presence":{"stars":null,"forks":null,"downloadsWeek":null,"license":null,"lastPushAt":null,"score":8}}}},"grade_history":[],"reviews":[]}