What happens if spaCy model fails to download?
The system automatically falls back to built‑in regex detection. Accuracy may slightly decrease, but the server continues to run.
@Amywith
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"llm-guard-mcp": {
"command": "python",
"args": [
"-m",
"spacy",
"download",
"zh_core_web_sm",
"--index-url",
"https://pypi.tuna.tsinghua.edu.cn/simple/"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A privacy protection gateway based on the MCP (Model Context Protocol) protocol. It detects and filters sensitive information in LLM applications—automatically masking Chinese names, phone numbers, emails, intercepting API keys and RSA private keys, and blocking custom blacklist words. It wraps these capabilities as MCP tools, returning JSON results.
Install dependencies with pip install -r requirements.txt. Use the Python function sanitize_prompt_mcp or the LLMSanitizer class. To run as an MCP server, execute python mcp_server.py or use the provided Docker image.
The system automatically falls back to built‑in regex detection. Accuracy may slightly decrease, but the server continues to run.
No. Once dependencies are installed, all data filtering and detection is performed locally. The MCP server can run fully offline.
Edit the `LLMSanitizer` class in `llm_guard.py` to modify the blacklist vocabulary or other detection rules.
All exceptions are caught gracefully. For example, if the presidio library fails, the system uses built‑in regex patterns without crashing the entire application.
All data processing happens locally on the machine running the server, even when deployed via Docker. No sensitive data is sent to external services.

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