"Understanding Integrated Knowledge Graphs" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Hybrid search, schema introspection and record read/write over FoxNose collections
MCP facade over the Nebelus Construction API. ~48 tools give full agent build parity: create/update/probe agents, edit graphs, attach knowledge and vector stores, wire connectors, set governance policies and locked guardrails, enable grounding-trace, and read deployment wiring. Purpose-built for regulated industries: data residency is enforced per region (EU / GCC-KSA), with PII controls and an audit trail. Agents are created as drafts — no deploy tool is exposed over MCP by design; publishing happens in the Nebelus console.
Persistent knowledge graph for AI-augmented teams. Store decisions, findings, and standing rules across agent sessions with semantic search and typed connections. Includes cross-session memory, audit trail, workspace isolation, and secret detection. Built for teams running agents that need to remember. Free until launch with team tier as default, anon trial available.
USECREA A persistent project layer for AI agents. USECREA keeps project knowledge, tasks, and progress available across different AI agents, computers, and work environments. Switch agents without copying chats, creating summary files, or starting over.
Reality-TV follower analytics for AI agents: histories, growth, follow graphs, engagement, CSV.
Open scientific and engineering knowledge for AI agents: search, evidence, document publishing.
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Ask questions in plain language, get answers from your business database. Cayra connects to SQL databases (PostgreSQL, MSSQL, MySQL, Oracle) and translates natural language to SQL — no SQL knowledge required.
Memwyre is an MCP-native persistent memory layer for AI agents, synchronizing context across Claude Code, Cursor, VS Code, and OpenClaw. Built with a high-precision retrieval architecture (dense vector search, BM25, and cross-encoder reranking), Memwyre achieves a benchmarked 73.1% accuracy on the Long-Context Memory (LoCoMo) benchmark. It provides secure, isolated knowledge vaults with dedicated tools (search_memwyre, save_memory, list_memories) to save and recall structured project decisions.
