"A server for Matomo analytics integration" matching MCP servers:

Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

MCP server for NodeBB forums that enables AI assistants to access forum data, search topics, and perform semantic search via the Model Context Protocol.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Implements the Model Context Protocol to retrieve context from a locally running Llama model, enabling AI applications to query and receive relevant information.

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.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

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

A bridging framework that integrates Knowledge Organization Infrastructure (KOI) with Model Context Protocol (MCP), enabling autonomous agents to exchange personality traits and expose capabilities as standardized tools.

Provides a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

MCP server for Obsidian vaults, optimised for Cursor agents — safe note editing, lean tool surface, direct filesystem access.

Persistent memory layer for MCP-compatible AI agents. Implements save/recall/search over a local SQLite session store via 14 MCP tools. Auto-loads relevant context at session start. No cloud dependency. Works with Claude, Cursor, Codex, Hermes Agent. Free (50 sessions) / Pro ($8/mo).

Versioned document vault system for MCP-compatible AI agents. Organizes reference knowledge (specs, contracts, rules) into per-project vaults with 36 MCP tools for inject, search, link, and version operations. Local SQLite backend. Works with Claude, Cursor, Codex, Hermes Agent. Free (3 vaults) / Pro ($9/mo).

Interacts with LogSeq via its API.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

Enables AI agents to query and modify RDF data, list named graphs, and access pre-built SPARQL templates on a Fuseki server, with optional HTTP Basic authentication.