synapseflow
by mrkingsleyobi
Overview
An MCP server that acts as a communication layer for a self-learning AI research assistant, orchestrating 66 specialized agents for cross-domain knowledge discovery and real-time insights.
Installation
cd synapseflow/mcp-server && npm run devEnvironment Variables
- HUGGINGFACE_API_KEY
- POSTGRES_URL
- REDIS_URL
- NEO4J_URI
- NEO4J_USERNAME
- NEO4J_PASSWORD
- JWT_SECRET
- BACKEND_URL
- MCP_SERVER_PORT
- MCP_SSE_ENABLED
- MCP_STDIO_ENABLED
Security Notes
Multiple critical security risks exist for deployment: 1. Hardcoded Default Passwords: `NEO4J_AUTH=neo4j/password` and `POSTGRES_PASSWORD=password` are hardcoded in `docker-compose.yml` and `init-databases.sh`. 2. Hardcoded JWT Secret: `JWT_SECRET: 'change-me-in-production'` is a default value in `backend/src/config/index.ts` and must be changed for production. 3. Exposed Database Ports: Database ports (PostgreSQL: 5432, Redis: 6379, Neo4j: 7474, 7687) are directly exposed in `docker-compose.yml`, which is unsafe for production without proper network isolation. 4. Reliance on Backend Security: The MCP server forwards requests to the backend; the overall system's security depends on the backend's robustness against injection or misconfigurations. 5. Dependency Security: Heavy reliance on numerous Ruvnet NPM packages, whose security is critical but outside this review's scope.
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