Redacts PII/PHI from text before it reaches an LLM. Fail-closed, self-hosted, no egress.
Umbryn MCP server exists for a simple reason — assistants are far more useful when they can act on Umbryn directly instead of describing what you should do. Redacts PII/PHI from text before it reaches an LLM. Fail-closed, self-hosted, no egress.
Teams building LLM and agent pipelines in regulated domains have no clean, drop-in way to strip PHI/PII from a payload before it crosses into a model provider's infrastructure. umbryn-mcp is that boundary: three MCP tools — redact, restore, detect — that scrub sensitive values into reversible placeholders, run entirely inside infrastructure you control, and block the request if detection is uncertain instead of leaking data.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
You will need 2 environment variables: EMPLOYEE_ID, MEDICARE_BENEFICIARY_ID. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by Rinava; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Umbryn's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"umbryn": {
"command": "uvx",
"args": ["umbryn-mcp"],
"env": {
"EMPLOYEE_ID": "your-value",
"MEDICARE_BENEFICIARY_ID": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| EMPLOYEE_ID | Configuration value read at startup. | Optional |
| MEDICARE_BENEFICIARY_ID | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.