"A server for finding security-related information and resources" matching MCP connectors:

Production-grade MCP server for discovering AI agent frameworks, vector databases, LLM gateways, and generating certified Docker Compose deployment stacks

Penfield MemoryAOAuth

io.github.penfieldlabs

Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.

aipricepatternsA

com.aipricepatterns

Historical market memory for AI agents using semantic vector search across years of financial market data. Discover similar market regimes, price patterns, and market context for quantitative research and algorithmic trading.

Search and read Vector Panda docs: API operations, pricing, storage tiers, measured benchmarks.

Cloudflare Workers MCP server: embedding-search

Shared cross-LLM long-term memory over MCP: semantic recall, sessions, and media (pgvector).

Disposable private vector search + semantic RAG for AI agents. x402 pay-per-call, no account.

NeuroStack

io.github.raphasouthall

Token-efficient MCP memory for Markdown vaults. Tiered search, GraphRAG, AI memories.

AmberOAuth

com.ambermem

Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.

SearchStackAOAuth

dev.searchstack

Agent-driven search: build, import, tune, search, and score result quality — all over MCP.

Remote ChromaDB vector database MCP server with streamable HTTP transport

Operate your Vector Panda collections: search and upsert vectors, schema, measured indexes, exports.

RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…

Xata MCP server lets AI agents interact with your Xata projects, and Postgres database branches.

Vector RAG store for Word/Excel/PDF/PowerPoint. Break-even pricing, $5 per 5,700 pages.

NeuralBrain MCP Server - RAG, Vector Memory, LLM Routing, Agent Identity, x402 Payments

Persistent memory and vector search for AI agents. Hosted, OAuth-protected via Google sign-in.

Persistent memory and knowledge graph for AI assistants — keyword + vector + graph search.

Same functionality, consuming only 1/20 of the context window tokens.