wardcat-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@wardcat-mcpscan this: 'My SSN is 123-45-6789'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
wardcat-mcp
An MCP server that exposes wardcat's on-prem PII detection and anonymization as tools any agent can call — Claude Desktop, Cursor, a self-hosted bot, a RAG pipeline. Use it as a guardrail: sanitize inputs before they reach an LLM, or gate them with a semantic "is this sensitive?" check.
Runs locally, stays local. The server runs on your machine over stdio; the text, the models, and all detection stay on-prem — nothing is sent anywhere. Publishing this package ships code you run yourself, not a hosted service.
Tools
Tool | Description |
| Detect PII and return the sanitized text plus a PII-free summary — entity types, actions, confidence. The summary never carries raw values; |
| Like |
| Holistic LLM yes/no on whether the text contains sensitive information. Requires the LLM layer ( |
| Report the enabled entity types, the default action, and whether the NER / LLM layers are active — so an agent can discover capabilities without trial and error. |
All tools return structured output (a typed schema, not a JSON string) and are annotated read-only.
Threat model — what this protects. wardcat-mcp guards what leaves the agent: it sanitizes text before it is logged, stored, or forwarded to a downstream API. It does not hide anything from the host LLM that is orchestrating the tool call — by the time a model invokes
scan, it has already read the raw text (and it may be retained in that provider's context or logs). To filter text before it reaches any LLM, call the wardcat library in-process instead.
Related MCP server: MCP Presidio
Install & run
Not published to PyPI — run it straight from the repository (its wardcat dependency does come from PyPI, so this needs no other source):
# Run directly from GitHub, no install:
uvx --from git+https://github.com/oguzhantopcu0/wardcat-mcp.git wardcat-mcp
# from a local clone:
uv run wardcat-mcp
# with the SpaCy NER layer (PERSON/ORG/ADDRESS), from a clone:
uv run --extra ner wardcat-mcpAdd it to an MCP client
Claude Desktop (claude_desktop_config.json), Cursor, Cline, Zed, etc.:
{
"mcpServers": {
"wardcat": {
"command": "uvx",
"args": ["--from", "git+https://github.com/oguzhantopcu0/wardcat-mcp.git", "wardcat-mcp"],
"env": {
"WARDCAT_SALT": "your-secret-salt",
"WARDCAT_ACTION": "redact",
"WARDCAT_LLM_MODEL": "llama3.2:3b"
}
}
}
}Configuration (environment variables)
Var | Default | Meaning |
|
| Hashing salt (required for the |
| broad structural + name set | Comma-separated entity types to enable. |
|
|
|
| — | Enable SpaCy NER with this model (needs the |
| — | Enable the on-prem LLM layer via Ollama (e.g. |
|
| Ollama endpoint. |
Development
uv sync --dev
uv run pytest # deterministic, regex-only — no models or network needed
uv run ruff check .
uv run mypy srcDisclaimer
wardcat is a best-effort PII detector — it does not catch everything and is not legal advice or a substitute for compliance review (e.g. GDPR/KVKK). Validate it against your own data. Provided "as is" (MIT).
License
MIT — see LICENSE.
Maintenance
Resources
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