American Default Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited American Default Mcp (MCP Server) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
A Model Context Protocol server that exposes American Default Research data — 96 economic distress indicators, the American Distress Index (ADI) composite score, and county-level distress scores across all 3,144 U.S. counties — to MCP-compatible AI agents.
Official MCP Registry namespace: org.americandefault/research Hosted endpoint: https://mcp.americandefault.org/mcp (streamable HTTP) Website: https://americandefault.org/press/mcp/
Point any MCP-compatible client at the hosted streamable-HTTP endpoint. No install, no data files, no maintenance — every response is generated against the same data that powers americandefault.org.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"american-default-research": {
"url": "https://mcp.americandefault.org/mcp",
"transport": "streamable-http"
}
}
}Restart Claude Desktop. The 5 tools appear under the hammer icon.
The MCP is also available via the Smithery gateway at smithery.ai/servers/americandefault/research.
Any client that speaks streamable HTTP can connect by adding the endpoint URL to its MCP server config. The exact config format varies by client — see your client's docs.
| Tool | Input | Returns |
|---|---|---|
get_indicator(slug) | bundle slug (e.g. the-buffer) | compact snapshot + pre-computed aggregates + canonical citation |
get_county_scorecard(fips) | 5-digit FIPS (4-digit accepted with implicit leading zero) | CDI scorecard + 5-domain breakdown + pre-baked citations |
get_adi_composite() | (none) | latest quarter ADI + 5 components + zone + citation |
search_indicators(query, limit=10) | keyword + optional limit (max 50) | ranked matches (slug, branded_name, name, category, URL) |
get_cross_correlations(slug) | indicator slug | fully-validated leading/lagging pairs split into as_leader + as_follower |
Every response carries schema_version: "v1". Breaking changes ship as a new tool with a _v2 suffix — v1 tools stay live for backward compatibility. Callers should assert the schema version they expect.
| Endpoint | Budget | Typical |
|---|---|---|
get_indicator | ≤ 16 KB | ~13.8 KB |
get_county_scorecard | ≤ 25 KB | ~2.5 KB |
get_adi_composite | ≤ 4 KB | ~2.0 KB |
Raw 300+ point indicator series is intentionally omitted from get_indicator to keep LLM context budgets manageable. The full series lives at https://americandefault.org/api/indicators/{slug}.json.
Every response includes a citation object with APA, MLA, Chicago, and news-copy forms. Three-tier naming is enforced:
See https://americandefault.org/llms.txt § "Canonical Attribution" for the authoritative spec.
The recommended way to use this MCP is the hosted endpoint above. The local install path is provided for transparency, audit, and self-hosting — but the local server reads data files from sibling directories (data/ and site/src/data/) that aren't included in this repo. To run locally end-to-end you need either:
https://americandefault.org/api/indicators/{slug}.json and county scorecards at https://americandefault.org/api/counties/{fips}.json. A small companion script (not bundled) can fetch these into a local data/ mirror.Install:
python3 -m venv venv
./venv/bin/pip install -r requirements.txtProbe (confirms the server boots and discovers tools):
PYTHONPATH=. python3 -m scripts.machine_layer.mcp_server --probeThis emits a JSON handshake to stdout and exits 0 without entering the stdio loop. Use it in CI or as a smoke test.
Run the stdio loop:
PYTHONPATH=. python3 -m scripts.machine_layer.mcp_serverStdout is reserved for JSON-RPC framing. Logs go to stderr.
The server is built on mcp >= 1.27.0 and supports two transports:
mcp_server.py) — for local Claude Desktop / Cursor / IDE pluginshttp_app.py) — for the hosted endpoint at mcp.americandefault.orgThe HTTP transport adds a bearer-auth middleware (anonymous + issued tiers), two-level token-bucket rate limiting (per-minute burst + per-hour sustained), and per-tier rate limits. See http_app.py for the full middleware stack.
Source JSONs carry both indicator_id (snake_case) and slug (kebab-case). 91 of 96 indicators have slugs that DO NOT mechanically transform from their id — branded indicators use marketing names like the-buffer (id: savings_rate), the-horizon (id: ai_capability), the-pinch (id: census_htops_difficulty).
The server builds a boot-time bidirectional map by scanning every source JSON once (~100ms). Lookups are O(1) thereafter.
10 of 96 bundles ship without populated data — indicators tracked but not yet backfilled (AI job postings, ABA consumer discretionary, NMHC rent tracker, utility disconnections, etc.). These return status: "awaiting_population" with full metadata and a null latest_value. Agents can discover the slug exists without receiving phantom data.
Two-level token bucket keyed by IP and bearer-token contact:
MCP_RATE_LIMIT_RPM, default 60MCP_RATE_LIMIT_RPH, default 600Anonymous tier (no bearer) gets the default. Issued tier (valid bearer) gets a higher allowance configured server-side.
This MCP serves data sourced from FRED (Federal Reserve Economic Data), BLS (Bureau of Labor Statistics), NY Fed Household Debt and Credit Report, ATTOM Data Solutions, Mortgage Bankers Association, American Bankruptcy Institute / Epiq Systems, and additional primary government and industry sources. Data is updated daily via automated pipelines.
Per-indicator source attribution is included in every citation field returned by the server. The full source-attribution methodology is at https://americandefault.org/methodology/.
American Default Research is a nonpartisan data project tracking U.S. household financial distress. It publishes the American Distress Index (ADI) — a composite 0-100 score built from five statistically derived components — and the County Distress Index (CDI) for all 3,144 U.S. counties.
Website: https://americandefault.org Press: https://americandefault.org/press/mcp/ Methodology: https://americandefault.org/methodology/
MIT — see LICENSE.
Data is free to use with attribution per the canonical attribution block at https://americandefault.org/llms.txt.
Bug reports and feature requests welcome via GitHub Issues on this repo. Pull requests are reviewed against the data pipeline's correctness gates — see https://americandefault.org/llms.txt for the data-accuracy standard.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.