mcp-memory-service
Verified Safeby doobidoo
Overview
A Model Context Protocol (MCP) server providing persistent, semantic memory storage and retrieval capabilities for AI agents. It supports lightweight semantic reasoning (contradiction, causal inference), content chunking, multi-backend storage (SQLite-vec, Cloudflare, Hybrid), autonomous memory consolidation (decay, association, clustering, compression, forgetting), and real-time updates via SSE. It's designed for token-efficient interaction with LLMs.
Installation
python -m mcp_memory_service.mcp_serverEnvironment Variables
- STORAGE_BACKEND
- CLOUDFLARE_API_TOKEN
- CLOUDFLARE_ACCOUNT_ID
- EMBEDDING_MODEL_NAME
- MCP_SERVER_PORT
- MCP_SERVER_HOST
- OAUTH_ENABLED
- OAUTH_CLIENT_SECRET_KEY
- OAUTH_JWT_PRIVATE_KEY_PEM
- MCP_MEMORY_OFFLINE
- LOG_LEVEL
- HF_HOME
- TRANSFORMERS_CACHE
Security Notes
The server employs good security practices, such as lazy initialization of storage, reliance on environment variables for sensitive data (e.g., Cloudflare API tokens, OAuth keys), and the generation of JWT keys rather than hardcoding. It uses `httpx` and `aiohttp` for external network calls, and `aiosqlite` with parameterized queries for database interactions, mitigating SQL injection risks. Document upload handlers attempt to prevent path traversal. `json.dump` is used for file writing, which is safer than `pickle`. Extensive use of `subprocess.run` occurs in installation and maintenance scripts, which is expected for such operations but could be a vector if those scripts are not carefully managed. Overall, no immediate critical vulnerabilities like `eval()` on untrusted input or hardcoded universal secrets were found in the core server logic, making it reasonably safe for its intended use case.
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