"Managing Kubernetes Clusters on AWS Elastic Beanstalk" matching MCP servers:

An Elasticsearch-based AI memory system optimized for Chinese that enables persistent knowledge storage and complex entity relationship management via the Model Context Protocol. It features advanced semantic search using the IK analyzer and supports multi-zone memory isolation for specialized knowledge graphs.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Self-hosted memo knowledge base with MCP, REST, and read-only web interfaces; lets AI agents save and retrieve tagged, searchable study notes for AWS DevOps Pro exam prep.

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.

MCP server that gives AI agents persistent, tenant-scoped memory with semantic search and consolidation, using CockroachDB vector indexes and Amazon Bedrock embeddings.

Enables Claude to intelligently query and analyze your resume using RAG technology. Supports skill matching against job requirements and answering questions about your professional background from locally stored resume files.

A redaction engine and MCP server that scrubs sensitive identifiers (AWS keys, IPs, emails, etc.) from AI agent traces, enabling safe storage and sharing of war stories via submit, search, and retrieve tools.

An MCP server that turns any compatible AI agent into a Zettelkasten partner for creating atomic notes, forming semantic links, detecting clusters, and synthesizing insights from your existing knowledge.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

A Model Context Protocol server that provides persistent storage, categorization, and retrieval of bookmarks through AI clients like Claude. It allows users to manage web links using custom categories and includes options for local JSON storage or AWS S3 synchronization.

Your coding agent can read the code. It can't read the argument behind it. lema-mcp gives your agent the recorded why — and the alternatives a project already ruled out — cited to the source. For React, Kubernetes, and Rust out of the box, and for your own repo with one command.

Provides persistent tribal memory for Claude Code sessions, enabling querying of decisions and entities, logging artifacts, and managing tasks with Supabase.

MCP server for managing verifiable LangGraph agent state on Walrus, enabling exact checkpoint save/load/resume and semantic search via MemWal.

Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.

This MCP server provides access to DEVONthink functionality via the Model Context Protocol (MCP). It enables listing, searching, creating, modifying, and managing records and databases in DEVONthink Pro on macOS.

Enables managing personal vehicle history including mileage, maintenance, expenses, reminders, and document references through MCP tools and resources, all stored locally in SQLite.

Provides a unified block-spindle-pscale function for reading, writing, and managing locks on JSON blocks across federated beaches, sed collectives, and bilateral grains.

MCP server for managing swipe files (sample/reference data) with tools to add, search, and update entries via Claude.

Enables AI agents to interact with the AI Network blockchain by managing accounts, submitting transactions, and reading database values. It supports registering Hyper Agents and accessing a Layer 2 DAG-based shared agent memory system based on staking status.

Enables AI agents to share persistent, conflict-safe memory by providing tools to recall, learn, reinforce, and retire lessons, using CockroachDB for storage and AWS Bedrock for embeddings.