synapseflow
Verified Safeby dynastynodes
Overview
A self-learning AI research assistant that orchestrates multiple AI agents and RAG pipelines to accelerate cross-domain knowledge discovery and hypothesis generation from academic papers.
Installation
cd synapseflow/mcp-server && npm run devEnvironment Variables
- HUGGINGFACE_API_KEY
- POSTGRES_URL
- REDIS_URL
- NEO4J_URI
- MCP_SERVER_PORT
- BACKEND_URL
- NEXT_PUBLIC_API_URL
- NEXT_PUBLIC_MCP_URL
- JWT_SECRET
- GPU_ENABLED
Security Notes
The project demonstrates strong security practices: secrets are managed via environment variables (e.g., HUGGINGFACE_API_KEY, JWT_SECRET), input is validated using Zod, and rate limiting is implemented. CORS is configured, and the architecture uses a layered approach where raw input from MCP (SSE/stdio) is passed to a backend API which performs rigorous validation. Containerization via Docker enhances isolation. The PRD explicitly addresses data privacy, API security, and MCP security, suggesting a thoughtful approach to potential vulnerabilities.
Similar Servers
gpt-researcher
The GPT Researcher MCP Server enables AI assistants to conduct comprehensive web research and generate detailed, factual, and unbiased reports. It supports multi-agent workflows, local document analysis, and integration with external tools via the Machine Conversation Protocol (MCP) for various research tasks.
deep-research
Generate comprehensive, AI-powered deep research reports, leveraging various LLMs and web search engines, with local knowledge base integration and report artifact editing.
aleph
Enables AI assistants to analyze documents too large for their context window by providing tools for search, exploration, and computation over massive datasets, implementing a Recursive Language Model (RLM) approach.
thinkingcap
A multi-agent research MCP server that runs multiple LLM providers in parallel and synthesizes their responses to a given query.