"Overview and Applications of Mixture of Experts in Machine Learning" matching MCP servers:

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

A multi-agent orchestration system that enables multiple Claude instances to collaborate through a centralized hub with a shared workspace and real-time communication. It features integrated task management, role assignment, and persistent memory to facilitate complex, synchronized agent workflows.

A marketplace of 13 specialist AI agents for coding assistants, offering tools to hire agents, run scripts, audit code, and manage vector stores with async job execution.

Enables autonomous learning from interactions through pattern recognition and machine learning techniques. Continuously improves performance by analyzing tool usage, providing predictive suggestions, and sharing knowledge across MCP servers.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Enables interaction with Cloudera Machine Learning to manage projects, files, and jobs through the Model Context Protocol. It supports tasks such as uploading files, scheduling jobs, and managing runtimes via natural language interfaces.

Enables AI agents to autonomously request services from other specialized agents and compensate them via x402 micropayments. Demonstrates a Machine-to-Machine economy using A2A protocol for agent communication, MCP for context management, and blockchain-based payments on Base network.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables Claude Code instances to discover each other on a local machine and exchange messages instantly, with a broker that coordinates triage and delegation across a peer mesh.

An MCP Server that provides access to League of Legends statistics via the SportData.io API, allowing agents to query and analyze LoL competitive gaming data.

Enables local opencode agents to control a live Chrome browser via MCP tools, including tab management, JavaScript execution, clicking, form filling, page reading, screenshots, and console log retrieval.

An MCP server that enables AI agents to execute real-world actions through 10 specialized engines covering authenticated API calls, browser automation, visual QA, shell commands, file operations, job scraping, and parallel task execution.

Enables AI assistants to interact with the QuantConnect cloud platform for algorithmic trading. Supports creating and managing trading strategies, running backtests, deploying live algorithms, and performing comprehensive trading operations through the QuantConnect API.

Enables AI agents to discover and execute tools via a secure MCP server with JWT authentication, RBAC, rate limiting, and audit logging.

An MCP server that lets AI agents delegate domain-specific tasks to local Ollama models, using purpose-built specialists for structured tasks like config generation, parsing, and validation.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.