# Georgia Civic Data (remote · mcp.georgiacivicdata.org)

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

- Trust score: 71/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-20

## Components

- remote · `mcp.georgiacivicdata.org`: 71/100 (this document), [markdown](https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp.md), [page](https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp)

## Channel facts

- Endpoint: `https://mcp.georgiacivicdata.org/mcp/`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.1.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-20.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 12 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 88/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 5943 tokens (~66/item across 89 items; 12 tools + 77 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 43/100
  - Stability observed for 13 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 79/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 27% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 12 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 14 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the Georgia Civic Data MCP server?

Georgia Civic Data is a hosted endpoint at https://mcp.georgiacivicdata.org/mcp/, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http org-georgiacivicdata-georgia-civic-data 'https://mcp.georgiacivicdata.org/mcp/'
```

### Cursor

```json
{
  "mcpServers": {
    "org-georgiacivicdata-georgia-civic-data": {
      "url": "https://mcp.georgiacivicdata.org/mcp/"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "org-georgiacivicdata-georgia-civic-data": {
      "type": "http",
      "url": "https://mcp.georgiacivicdata.org/mcp/"
    }
  }
}
```

### Codex

```toml
[mcp_servers.org-georgiacivicdata-georgia-civic-data]
url = "https://mcp.georgiacivicdata.org/mcp/"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "org-georgiacivicdata-georgia-civic-data": {
      "type": "remote",
      "url": "https://mcp.georgiacivicdata.org/mcp/",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add org-georgiacivicdata-georgia-civic-data --url 'https://mcp.georgiacivicdata.org/mcp/' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  org-georgiacivicdata-georgia-civic-data:
    url: "https://mcp.georgiacivicdata.org/mcp/"
```

### Netclaw

```json
{
  "McpServers": {
    "org-georgiacivicdata-georgia-civic-data": {
      "Transport": "http",
      "Url": "https://mcp.georgiacivicdata.org/mcp/"
    }
  }
}
```

### Vellum

```bash
assistant mcp add org-georgiacivicdata-georgia-civic-data -t streamable-http -u 'https://mcp.georgiacivicdata.org/mcp/'
```

### Other

```json
{
  "mcpServers": {
    "org-georgiacivicdata-georgia-civic-data": {
      "type": "http",
      "url": "https://mcp.georgiacivicdata.org/mcp/"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-19 (score 71, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-17 (score 70, +1)

- [security] The server rewrote its instructions, which are the text every model session reads

### 2026-09-15 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-13 (score 68, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-12 (score 67, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “describe_dataset” rewrote its description, which is the text the model reads
- [security] Tool “get_contract” rewrote its description, which is the text the model reads
- [security] Tool “link_query” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 5329 → 5888
- [functional] New resource “census/acs/educational_attainment contract”
- [functional] New resource “census/acs/employment contract”
- [functional] New resource “census/acs/median_home_value contract”
- [functional] New resource “census/acs/median_household_income contract”
- [functional] New resource “census/acs/population_by_age contract”
- [functional] New resource “census/acs/population_by_race contract”
- [functional] New resource “census/acs/poverty contract”
- [functional] New resource “census/acs/school_enrollment contract”
- [functional] New resource “immigration/deportation_data/eoir_case_outcomes contract”
- [functional] New resource “immigration/deportation_data/ice_arrests contract”
- [functional] New resource “immigration/deportation_data/ice_arrests_with_detention_stays contract”
- [functional] New resource “immigration/deportation_data/ice_detainers contract”
- [functional] New resource “immigration/deportation_data/ice_detention_stays contract”
- [functional] New resource “immigration/deportation_data/ice_detention_stints contract”
- [functional] New resource “immigration/deportation_data/ice_removals contract”
- [functional] New resource “immigration/ga_jails/quarterly_detainer_reports contract”
- [cosmetic] “query_dataset” reworded the description of “main_topic”

### 2026-09-11 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-09 (score 66, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-08 (score 65, 0)

- [functional improvement] Stability: unverified → 0.03

## MCP tools (12)

### `list_datasets` (~173 tokens)

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.

### `search_datasets` (~135 tokens)

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).

Input parameters:

- `limit` (integer)
- `query` (string, required)

### `describe_dataset` (~374 tokens)

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.

Input parameters:

- `main_topic` (string)
- `topic` (string, required)
- `verbosity` (string): 'full' (default) or 'schema' (drops prose; keeps columns/filters/enums/key_metric/recommended_query).

### `query_dataset` (~644 tokens)

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.

Input parameters:

- `columns`: Project only these output columns (fact columns + joined label columns). Smaller pages / fewer column reads. Omit for all columns.
- `detail`: Grain (e.g. schools/districts/states); default finest.
- `filters`: 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 an…
- `include_labels` (boolean): Join district/school/county/demographic name columns (default true); false = codes only (faster, leaner).
- `limit`: Page size (default 100, max 500).
- `main_topic` (string): Main topic: 'education', 'census', or 'immigration'.
- `offset` (integer): Row offset for paging (>= 0).
- `order` (string): Sort direction for order_by: 'asc' or 'desc'.
- `order_by`: 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…
- `topic` (string, required): Topic name, e.g. 'act_scores'.
- `year`: Exact year. Use this OR year_min/max.
- `year_max`: Inclusive upper year bound (range).
- `year_min`: Inclusive lower year bound (range).

### `distinct_values` (~185 tokens)

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.

Input parameters:

- `column` (string, required)
- `detail`
- `limit` (integer)
- `main_topic` (string)
- `prefix`
- `topic` (string, required)

### `aggregate` (~385 tokens)

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.

Input parameters:

- `agg` (string)
- `detail`
- `filters`
- `group_by`
- `limit`
- `main_topic` (string)
- `metric`
- `offset` (integer)
- `order` (string)
- `order_by`
- `topic` (string, required)
- `year`
- `year_max`
- `year_min`

### `resolve_entity` (~160 tokens)

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).

Input parameters:

- `kind` (string, required)
- `limit` (integer)
- `query` (string, required)

### `describe_dimension` (~116 tokens)

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.

Input parameters:

- `name` (string, required)

### `get_dimension` (~100 tokens)

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.

Input parameters:

- `limit`
- `name` (string, required)
- `offset` (integer)

### `get_contract` (~110 tokens)

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.

Input parameters:

- `fmt` (string)
- `kind` (string)
- `main_topic` (string)
- `name` (string, required)

### `link_tables` (~185 tokens)

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.

Input parameters:

- `tables`

### `link_query` (~257 tokens)

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.

Input parameters:

- `limit`
- `sql` (string, required)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp#diagnostics

## Score history

- 2026-09-20: 71
- 2026-09-19: 71
- 2026-09-18: 70
- 2026-09-17: 70
- 2026-09-16: 69
- 2026-09-15: 69
- 2026-09-14: 68
- 2026-09-13: 68
- 2026-09-12: 67
- 2026-09-11: 67
- 2026-09-10: 66
- 2026-09-09: 66
- 2026-09-08: 65
- 2026-09-07: 65

## Common questions

### What is the Georgia Civic Data MCP server?

Georgia Civic Data is an MCP server listed in the public MCP registry as org.georgiacivicdata/georgia-civic-data. Georgia education, Census, and immigration data: query, filter, aggregate, and link datasets. This page covers its hosted endpoint (https://mcp.georgiacivicdata.org/mcp/).

### Is the Georgia Civic Data MCP server safe to use?

Georgia Civic Data scores 71 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Georgia Civic Data MCP server expose?

Georgia Civic Data exposes 12 tools: list_datasets, search_datasets, describe_dataset, query_dataset, distinct_values, and 7 more. Their descriptions and schemas cost roughly 2,824 tokens of context every time the server is loaded.

### Does the Georgia Civic Data MCP server require authentication?

No. We connected to Georgia Civic Data without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the Georgia Civic Data MCP server still maintained?

Georgia Civic Data is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://mcp.georgiacivicdata.org/mcp/
- Repository: https://github.com/shanejorr/georgia-data
- Website: https://georgiacivicdata.org/mcp
- Changelog RSS feed: https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp.json
- HTML version of this page: https://verifymcp.io/servers/org-georgiacivicdata-georgia-civic-data/mcp