关键词 "Dynamic prompting" 的搜索结果， 共 24 条, 只显示前 480 条
An MCP server that hosts finite state machines as dynamic resources that multiple clients can subscribe to and be updated when their state changes.
npx -y convex@latest mcp aConvex’s MCP server lets you introspect tables, call functions, and read/write data seamlessly. Agents can generate one-off queries safely—thanks to Convex’s sandboxed querie
Mirror of
MCP server that creates its own tools as needed
An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.
An MCP server allowing AI assistants to use a Neo4j knowledge graph as their primary, dynamic instruction manual and long term project memory with adaptive templating and autonomous tool development t
Branch-Thinking MCP Tool A TypeScript-powered MCP server for managing parallel branches of thought, semantic cross-references, and persistent tasks. Features dynamic scoring, AI-generated insights, ba
This project demonstrates a client-server architecture using a custom MCP (Model Configuration Protocol) server integrated with MCP tools to dynamically generate and return React + TypeScript code.
Instead of dumping 100+ tools into a model’s prompt and expecting it to choose wisely, the Unified MCP Tool Graph equips your LLM with structure, clarity, and relevance. It fixes tool confusion, preve
The open-source multi-agent chat interface that lets you manage multiple agents in one dynamic conversation and add MCP servers for deep research
This is a lightweight, plug-and-play MCP server that empowers any LLM to dynamically search and retrieve up-to-date documentation from popular AI libraries such as LangChain, LlamaIndex, and OpenAI.
Using Advanced AI Prompting to enhance LLM planning to solve complex math problems and draw the answer on MSPaint Canvas
ᑭᑫᓐᑖᓱᐎᓐ ᐋᐸᒋᒋᑲᓇᓐ - Gikendaasowin Aabajichiganan - Cognitive Tools MCP server implemented from various prompting strategies.
Developed an MCP-based AI infrastructure enabling real-time tool execution, structured knowledge retrieval, and dynamic agentic interactions for AI clients like Claude and Cursor.
An MCP server that enables dynamic, reflective problem-solving by structuring thought processes and automatically logging each session to Recall.
This project demonstrates how to create and use a Model Context Protocol (MCP) server that can provide custom tools and resources to AI assistants like Claude and others that support the MCP standard.
The OpenAPI to Model Context Protocol (MCP) proxy server bridges the gap between AI agents and external APIs by dynamically translating OpenAPI specifications into standardized MCP tools. This simplif
Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents
A centralized manager for Model Context Protocol (MCP) servers with dynamic server management and monitoring
Enhanced MCP memory system with dynamic compression, context management and thinking process integration. Based on the original MCP memory server.
MCPchat is terminal-based LLM chat client supporting multiple Model Context Protocol (MCP) servers with real-time server management and dynamic model switching.
Lovart 全球首个设计 Agent 体验 Lovart 的三个特点： 一、全链路设计和执行，一句话搞定 以前的文生图工具，它们所提供的任务是“生成图片”这一环。 而设计 Agent，则像一位“设计执行官”，覆盖从创意拆解到专业交付的整个视觉流程。 从意图拆解 → 任务链 → 最后成品，一句话全搞定。 单次可以执行上
Mujoco（Multi-Joint dynamics with Contact）是一款用于机器人学、生物力学等领域的高性能物理仿真引擎，其核心功能包括动力学模拟、接触力建模及多关节系统仿真。该工具提供直观的操作界面、丰富的物理参数配置以及灵活的约束条件设置，适用于复杂机械系统或生物运动的模拟分析。以下从操作功能、仿真交互机制、核心术语与参数三个维度展开说明。 MuJoCo是“多关节接触动力学”
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