databutler-provenance
Live trust signals for domains and software packages, computed per request from RDAP, npm and PyPI: registration age, registrar, nameservers, maintainers…
What it can do
- Domain Provenance: Who runs this domain? Live RDAP lookup: registration date and age, registrar, nameservers, status, whether the registrant is redacted, plus a typosquat check (edit-distance / brand-
- Package Provenance: Who publishes this package, and is it a typosquat? Live npm or PyPI lookup: first-publish date and age, release count, latest version, maintainers/author, linked repo, plus a typos
What data it sees
Do you need an account
No: the server works without sign-in
Live trust signals for domains and software packages, computed per request from RDAP, npm and PyPI: registration age, registrar, nameservers, maintainers, linked repo, and homoglyph-aware typosquat resemblance to well-known brands and packages. Descriptive signals, not a verdict — use before trusting an unfamiliar domain or dependency. Free, no auth. From databutler.dev.
How to connect
How to connect databutler-provenance to Claude
The server address to paste into Claude settings:
https://databutler-provenance--drchriseames.run.tools
- Open Claude (claude.ai or the desktop app).
- Go to Settings → Connectors.
- Click Add custom connector.
- Paste the server address copied below into Remote MCP server URL and click Add.
- In a chat, click + → Connectors and switch the new connector on.
Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.
Authorization
After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.
How to connect databutler-provenance to ChatGPT
The server address to paste into ChatGPT settings:
https://databutler-provenance--drchriseames.run.tools
- Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
- Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
- Open Settings → Connectors and click Create.
- Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
- Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
- In a new chat open + → Apps/Connectors and enable the connector.
OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.
Authorization
On first use ChatGPT opens the service's sign-in window. Sign in and approve access.
How to connect databutler-provenance to Cursor
The server address to paste into Cursor settings:
https://databutler-provenance--drchriseames.run.tools
Fastest: click Open in Cursor below and confirm the prompt.
Manually:
- In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
- Paste the JSON copied below into
~/.cursor/mcp.json(per project:.cursor/mcp.jsonin the repo root). - Save the file. The server appears in the MCP list; authorize it there if asked.
Authorization
If the service needs sign-in, an authorize button appears next to the server in the MCP list.
Server tool list (2)
Raw names from tools/list. Only developers need these.
| domain_provenance | Who runs this domain? Live RDAP lookup: registration date and age, registrar, nameservers, status, whether the registrant is redacted, plus a typosquat check (edit-distance / brand-substring) against high-value brands. A very recently registered domain resembling a bank or big brand is a classic phishing signal — but report it as a signal, not a conclusion. |
| package_provenance | Who publishes this package, and is it a typosquat? Live npm or PyPI lookup: first-publish date and age, release count, latest version, maintainers/author, linked repo, plus a typosquat check against popular package names. Use when an agent is about to install or recommend an unfamiliar dependency. |