About ai-vendor-data-trust-index
A source-cited index of data-handling policies for 11 AI products, for teams comparing training use, retention and opt-out options. Verbatim policy quotes, confidence levels and weekly checks make the evidence visible.
Key features
- 01Cited vendor policy quotes
- 02Training, retention and opt-out fields
- 03Confidence and check-date records
- 04Weekly automated policy checks
- 05Headline and cross-vendor comparison tools
Use cases
- 01Compare data practices across AI vendors
- 02Check a vendor’s cited policy details
- 03Review changes flagged by automated checks
MCP support
The remote MCP server exposes tools for a vendor’s cited headline, comparison of one field across vendors, and retrieval of a full cited record. The full-record tool is paid; the other two are free.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://trust.oscar-lopez.com/mcpclaude mcp add --transport http oscarlopez-gif-ai-vendor-data-trust-index https://trust.oscar-lopez.com/mcp{"mcpServers":{"oscarlopez-gif-ai-vendor-data-trust-index":{"type":"http","url":"https://trust.oscar-lopez.com/mcp"}}}Open in Cursor{"servers":{"oscarlopez-gif-ai-vendor-data-trust-index":{"type":"http","url":"https://trust.oscar-lopez.com/mcp"}}}Open in VS Code{"mcpServers":{"oscarlopez-gif-ai-vendor-data-trust-index":{"type":"http","url":"https://trust.oscar-lopez.com/mcp"}}}{"mcpServers":{"oscarlopez-gif-ai-vendor-data-trust-index":{"serverUrl":"https://trust.oscar-lopez.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
3| Parameter | Type | Required | Description |
|---|---|---|---|
| vendor | string | Yes | vendor id, e.g. 'openai-api', 'anthropic-api' |
Details
From the project READMEAI Vendor Data-Trust Index
A source-cited record of how AI products handle your data — whether the vendor trains on your
inputs, whether zero data retention is available, and whether you can opt out — across 11
products (OpenAI, Anthropic, Google Vertex & Gemini, Azure OpenAI, AWS Bedrock, Microsoft 365 Copilot,
GitHub Copilot, Mistral, Meta Llama). Every value carries a verbatim quote from the vendor's live
policy, a source URL, a confidence level, and the date it was checked. Ambiguous cases are marked
unclear rather than guessed. An automated verifier re-checks the policies weekly and only publishes
a value when a verbatim quote supports it — otherwise it holds the last human-verified value.
- Live index (human view): https://data.oscar-lopez.com/ai-trust
- Free comparison API:
GET https://trust.oscar-lopez.com/v1/ai-trust/matrix - Cite as: López, O. E. (2026). AI Vendor Data-Trust Index. https://data.oscar-lopez.com/ai-trust
For AI agents (MCP)
This is a remote MCP server (streamable-http). Add it to your MCP client:
{
"mcpServers": {
"ai-vendor-data-trust": {
"type": "streamable-http",
"url": "https://trust.oscar-lopez.com/mcp"
}
}
}
Tools: get_ai_vendor_trust_headline (free — verdict + cited fields for one vendor),
get_ai_vendor_trust_compare (free — one field across all vendors), get_ai_vendor_trust
(paid, x402 — full cited record). Vendor IDs: openai-api, openai-consumer, anthropic-api,
google-vertex, google-gemini-consumer, microsoft-copilot, azure-openai, aws-bedrock,
mistral-api, meta-llama, github-copilot.
Endpoints
Free (CORS-enabled):
GET /v1/ai-trust/headline?vendor=openai-api— verdict + 3 cited headline fieldsGET /v1/ai-trust/compare?field=trains_on_your_data— one field across all vendors
Paid (mock x402 — 402 challenge → echo {resource,nonce,mac} in X-PAYMENT):
GET /v1/ai-trust/vendor?vendor=openai-api— full cited recordGET /v1/ai-trust/all— bulk export
Other: POST /mcp (JSON-RPC: 2 free tools + 1 paid), GET /openapi.json,
GET /.well-known/x402, GET /admin/config?key=<STATS_TOKEN> (config lever).
Deploy
cd ai-trust-index
npm install
npm run db:create # → paste the returned database_id into wrangler.jsonc
npm run schema:remote # create tables (drops existing)
npm run seed:remote # load the 6-vendor provisional seed
npx wrangler deploy --dry-run --outdir dist # verify build (no auth)
npm run deploy # deploy the Worker
# In Cloudflare: add custom domain trust.oscar-lopez.com to this Worker.
# Set real secrets before public use:
npx wrangler secret put MOCK_PAYMENT_SECRET
npx wrangler secret put STATS_TOKEN
Verifier (v1) — automated weekly re-check
Pipeline in src/verifier.ts: fetch each vendor's pinned policy URLs → content-hash diff (skip if
unchanged) → model extraction of {value, quote} (Workers AI by default; frontier via AI Gateway if
configured) → verbatim quote-verification (the candidate quote must be a literal substring of the
fetched source, else rejected) → confidence gate. Only a quote-verified DEFINITE value auto-publishes
(verified_by='auto'); anything else is queued in review_queue and the last good published value is
held (never auto-downgraded to unclear). A failed fetch holds the last good value and flags the URL.
Runs weekly via cron (0 12 * * 1) and on demand.
Admin endpoints (key-gated by STATS_TOKEN):
GET /admin/verify?key=…[&force=1][&vendor=ID]— run the verifier nowGET /admin/review?key=…— list open review-queue candidatesGET /admin/review-resolve?key=…&id=N&action=approve|reject— approve (publishes ashuman) or rejectGET /admin/verify-selftest?key=…— proves the quote gate rejects a fabricated quote / accepts a verbatim one
Extractor: defaults to Workers AI (EXTRACTOR_MODEL, needs no external key). To use a frontier model,
set AI_GATEWAY_URL + AI_GATEWAY_TOKEN (secret) and point EXTRACTOR_MODEL at the provider model.
Not yet built
The /ai-trust site page's changelog view, real x402 settlement, breadth expansion into new vendors
(free tier) and the depth/channel schema (paid tier). See ../AI-Trust-Index_v1-Spec_Verifier-and-Depth.md.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.