LLM security APIs for Japanese applications, available as an MCP server.
nexus-mcp is an MCP project maintained in the nexus-api-lab/nexus-mcp repository. The maintainer describes its purpose as “LLM security APIs for Japanese applications, available as an MCP server”. Read the project documentation for the complete scope.
The maintainer’s configuration uses https://mcp.nexus-api-lab.com/. It declares streamable-http transport. Check the project’s account and authentication instructions when adding the endpoint to your client.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| get_trial_key | First — if you don't have an API key yet |
| check_injection | Before every user input reaches the LLM |
| validate_rag_input | At the RAG pipeline entry point (pass/fail gate) |
| sanitize_content | When external content is fetched to use as LLM context |
| get_pii_guard_key | First — if you don't have a PII Guard key yet |
| pii_scan | Before logging, storing, or forwarding Japanese user text |
{
"mcpServers": {
"nexus": {
"type": "http",
"url": "https://mcp.nexus-api-lab.com/"
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
Official Redis server — search keys, inspect data structures and manage caches in natural language.
Enables AI agents and LLMs to interact with the web and extract information from web pages via the RAG Web Browser Actor.
Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local
Augments AI responses with relevant documentation context through vector search.
Augment AI responses with context by retrieving and processing documentation through vector search.
Enables semantic analysis of chat conversations through vector embeddings and knowledge graphs.