"Guide to Deploying a Large Language Model Framework" matching MCP servers:

A middleware system that connects large language models (LLMs) with various tool services through an OpenAI-compatible API, enabling enhanced AI assistant capabilities with features like file operations, web browsing, and database management.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

Persistent 3D/2D spatial world model MCP server for AI agents. Entity tracking, object permanence, AABB collision simulation & view frustum projection via local SQLite.

AI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

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.

Enables MCP-aware clients to use a reliable multi-agent tool system with tiered model routing, schema-validated decisions, and graceful tool degradation, allowing small models to handle complex tasks.

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.

Orchestrates AI agents through structured markdown documents, enabling multi-agent workflows with automatic context injection and workflow management.

An event-driven MCP server that enables agents to share context streams, publish and subscribe to events, manage tasks, and follow protocols, keeping a fleet of agents mutually context-aware in real time.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

An MCP server that exposes tools for sub-agent style reasoning across multiple LLM providers, enabling delegation of prompts to various models and running critique loops, debates, red-teaming, and answer ranking.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

A Model Context Protocol server that converts Markdown content to HTML format.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables an AI assistant to build and revise an explicit shared world model, then reason, create, narrate, remember, and give writing feedback through inspectable processes, concepts, and cuts. Supports general-purpose modeling, storytelling, agent/user memory, and ideation over one engine and graph while keeping estimates, interpretations, and observations distinct.

Provides context management, task classification, and routing tools for small models (1B-12B) in production agentic workflows.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.