About regulatory-deadline
regulatory-deadline is a stdio MCP server for tracking deadlines related to FinCEN, SEC, the EU AI Act, and GDPR. It is aimed at AI agents that need regulatory deadline information.
Key features
- 01Track FinCEN deadlines
- 02Track SEC deadlines
- 03Track EU AI Act deadlines
- 04Track GDPR deadlines
- 05Override remote snapshot or use local data
Use cases
- 01Check relevant regulatory deadlines
- 02Track deadlines across financial and AI regulations
- 03Use a local snapshot without remote fetching
MCP support
It runs as a stdio MCP server and provides regulatory deadline tracking to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y @weiseer/regulatory-deadline-mcpREG_URLOverride remote snapshot URLREG_LOCAL_ONLYSkip remote fetchLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
4Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| regime | string | No | e.g. ai-act, gdpr, sec, fincen-boi |
| within_days | number | No | |
| jurisdiction | string | No |
Details
From the project README@weiseer/regulatory-deadline-mcp
FinCEN/SEC/EU AI Act/GDPR deadline tracker for AI agents. stdio MCP server.
Part of weiseer — AI-agent-native cached oracles.
Install
npm install -g @weiseer/regulatory-deadline-mcp
Use with Claude Desktop / Cursor / Cline / Continue / Windsurf
{
"mcpServers": {
"regulatory-deadline": {
"command": "npx",
"args": ["-y", "@weiseer/regulatory-deadline-mcp"]
}
}
}
Why use this instead of your agent doing it itself
The DIY cost in token-spend, latency, and rate-limit risk is 100-1500x our cost. See the weiseer organization README for the economic argument.
Environment
REG_URL— override remote snapshot URLREG_LOCAL_ONLY=1— skip remote fetch
License
Apache-2.0
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.