"Guide to Building a Minecraft Protocol (MCP) Server" matching MCP servers:

An MCP server that transforms books into agent-callable skill packages, enabling AI agents to use books as guides for evidence, procedures, and tutoring.

A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.

This MCP server lets any compatible AI assistant read, extend and connect the matters in a person's life — plans, trips, people and places — from a repository that belongs to the user, so a new assistant picks up where another left off. It keeps the user's words verbatim, records every change, and stores the repository as a plain file on the user's own computer.

Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.

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.

Resume-as-an-Agent (RAAA) or Portfolio-as-an-Agent (PAAA) allows Agents to talk to your resume, find out if you are available for work, and more. Use the MCP to allow recruiters, managers, and anyone with an agent to "talk" to your resume.

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.

An enhanced memory management system that wraps memento-mcp with sophisticated features including protocol enforcement, quality scoring, hybrid search strategies, and synthesis reports. Enables intelligent memory storage, retrieval, and analysis with automatic archival and confidence tracking.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

A semantic-memory MCP server that stores text 'memories' with provenance and enables recall by meaning (vector search), keyword (FTS5), or structured filters.

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.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Enables AI coding agents to save task context as a validated JSON document and later restore it as a concise briefing with the objective, status, relevant files and focus lines, decisions, and prioritized next steps.

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

A Model Context Protocol server that helps collect and structure project portfolio information through a guided conversation flow.

Helps AI agents decide what they are justified in believing from the evidence they have. EWP separates stored memory from verified knowledge, preserves conflicts and source lineage, and produces a deterministic warrant result that MCP clients can use consistently across different memory systems.

An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.

104 high quality skill packs across 20+ tech stacks. 1,284 curated chunks. 104 validated discoveries. Every piece reviewed, cross-validated, and myth-busted.

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

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

Enables AI agents to read, write, search, and manage persistent, decentralized context memory via a GCS-backed MCP server, with tools for document storage, retrieval, and STIM protocol nuggets.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Enables AI agents to record structured pitfalls (problem, root cause, solution), query experience others have already verified, check an agent's reputation, and repay trust signals when adopting someone else's findings. Read operations are anonymous while writes require a free identity credential.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

A stateful gateway server for AI applications that solves the memory limitations of Large Language Models by maintaining separate conversation contexts for each user.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Enables MCP clients to search a Zotero library, read items, take notes, organize collections, and perform semantic search over PDFs.