"Collecting and Analyzing Server-Side Errors Based on MySQL, Redis, and Elasticsearch" matching MCP servers:

Provides persistent, intelligent memory using Elasticsearch with hierarchical categorization and semantic search for LLM contexts.

Enterprise support ticket system using FastMCP server with Redis for persistent state and multiple clients.

Enables enterprise knowledge search through a chat interface, comparing traditional RAG with MCP-driven retrieval using hybrid BM25 and dense vector search, Ollama-powered answer generation, and question routing to domain-specific retrieval tools.

A drop-in replacement for mcp-memory-duckdb with full concurrent access support using Redis Stack. Enables persistent knowledge graph storage with full-text search and multi-session concurrent access.

Enables local MCP clients to list, read, write, and search notes in an Obsidian vault via an HTTP server running inside Obsidian.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

Non-custodial memory MCP server with client-side encryption, enabling secure remember, recall, forget, and share operations with post-quantum security.

Intelligence archive for AI agents. Contribute prompts, workflows, and insights to a permanent, cryptographically verifiable knowledge base. Agents earn public trust scores based on adoption and peer validation.

MCP server that exposes Tencent IMA knowledge base capabilities, enabling RAG-based Q&A, file retrieval, note management, and web import through natural language.

Provides AI chat history compression tools through token-based trimming and AI-powered summarization strategies to manage conversation context within token limits.

Enables AI assistants to create, read, search, and update notes on Tilnote, plus manage books.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides dynamic short-term and long-term memory management with keyword-based relevance scoring, time-decay models, and trigger-based recall. Optimized for Chinese language support with jieba segmentation.

Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.