envector-mcp-server
by CryptoLabInc
Overview
Provides a Model Context Protocol (MCP) server that enables AI applications to securely access and search homomorphically encrypted vector data stored in enVector, suitable for private networks and secure data environments.
Installation
python srcs/server.py --mode "http" --host "localhost" --port "8000" --server-name "envector_mcp_server" --envector-address "YOUR_ENVECTOR_HOST:50050" --envector-key-id "mcp_key" --envector-key-path "YOUR_KEY_PATH" --embedding-mode "femb" --embedding-model "sentence-transformers/all-MiniLM-L6-v2"Environment Variables
- MCP_SERVER_MODE
- MCP_SERVER_HOST
- MCP_SERVER_PORT
- MCP_SERVER_ADDRESS
- MCP_SERVER_NAME
- ENVECTOR_ADDRESS
- ENVECTOR_CLOUD_ACCESS_TOKEN
- ENVECTOR_KEY_ID
- ENVECTOR_KEY_PATH
- ENVECTOR_EVAL_MODE
- ENVECTOR_ENCRYPTED_QUERY
- EMBEDDING_MODE
- EMBEDDING_MODEL
- OPENAI_API_KEY
Security Notes
The `document_preprocess.py` module's `_load_documents_from_path` function takes a `path` parameter from tool arguments and uses `pathlib.Path(path)` and `root.glob(pattern)` to read files. If this `document_path` is controlled by an untrusted client (e.g., via a malicious AI prompt), it presents a significant path traversal vulnerability, potentially allowing unauthorized reading of files outside the intended directory or denial-of-service. There is no clear input sanitization for `document_path`. While configuration is handled via environment variables/CLI (avoiding hardcoded secrets), and the default HTTP bind host is localhost (improving default security), the path traversal risk is critical. The `_to_json_available` method's fallback to `repr(obj)` could potentially expose sensitive object states in logs/errors, but is a lesser concern.
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