"A security license quality system or service" matching MCP servers:

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

Self-hosted service providing unified memory search and write operations across file-based memory, vector embeddings, and the Graphiti temporal knowledge graph, accessible via HTTP API or MCP stdio transport.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

Provides self-hosted, versioned project memory with full-text and vector search, enabling search, retrieval, upsertion, and history management of project entries via MCP tools.

MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.

Enables AI agents to interact with Google NotebookLM and Gemini API for document analysis, deep research, and notebook creation with enterprise-grade security including post-quantum encryption and 14 security layers.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Provides hybrid retrieval (dense + BM25 + RRF) with collection-based isolation and document ingestion for private knowledge access via MCP.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with Adyen's Checkout Utility Service API through natural language, auto-generated using AG2's MCP builder.

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Jira Service Management (beta): https://www.cdata.com/download/download.aspx?sku=GKZK-V&type=beta

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.