Discover Awesome MCP Servers

Shell MCP Server

Mirror of

OpenWeather MCP Server

Previsão do tempo: Servidor MCP para previsão do tempo.

mcp-changtianML

Servidor MCP para acessar changtianML.

🧠 DeepSeek R1 Reasoning Executor

Um servidor MCP poderoso que aprimora as capacidades do Claude ao integrar o motor de raciocínio de ponta do DeepSeek R1.

mcp-atendeai

Servidor MCP para atendeAi

File Edit Check MCP Server

MCP server that enforces pre-read checks and detailed commit documentation

McpRails

MCP servers in your Rails app

Model-Context-Protocol Servers

All my MCP servers that i created

Marimo Documenation MCP Server

A Model Context Protocol (MCP) server that provides programmatic access to the Marimo Documentation.

MCP_claude

This is to demonstrate how an MCP server can be built for Claude Desktop MCP Client

Symbol MCP Server (REST API tools)

Symbol MCP Server. (REST API tools)

MCP LLM Bridge

A Simple bridge from Ollama to a fetch url mcp server

Hello, MCP server.

Um servidor MCP básico.

Malaysia Prayer Time for Claude Desktop

Um servidor de Protocolo de Contexto de Modelo (MCP) para dados de Horários de Oração da Malásia.

mcp-edge-search

Um servidor de Protocolo de Contexto de Modelo que habilita capacidades de pesquisa na web para clientes MCP como o Claude Desktop.

MCP Host Project

Showcases how to integrate Spring AI's support for MCP (Model Context Protocol) within Spring Boot applications, covering both server-side and client-side implementations.

Bear MCP Server

Mirror of

NYT MCP Server

Um servidor de Protocolo Concentrador de Mensagens (MCP) que fornece uma interface unificada e simples para as APIs do New York Times. Este servidor simplifica a interação com múltiplas APIs do NYT através de um único ponto de acesso.

Filesystem MCP Server

Mirror of

Prometheus Alertmanager MCP Server

A Model Context Protocol (MCP) server that integrates with Prometheus Alertmanager

Modes MCP Server

Mirror of

Confluence Communication Server MCP Server

Espelho de

Postgers_MCP_for_AWS_RDS

É um servidor MCP para acessar o banco de dados PostgreSQL no AWS RDS.

spring-mcp-server-sample

MCP Server Sample

XACHE - Crypto Trader Website

Goose AI com Servidores MCP

cursor_agents

Okay, I understand. You want to integrate a team of human experts into your agent flow using an MCP (presumably, you mean a platform like Microsoft Copilot Studio, formerly Power Virtual Agents, or a similar conversational AI platform). Here's a breakdown of how you can achieve this, along with considerations and best practices: **General Approach: Hand-off to Human Agents** The core idea is to detect when the agent cannot adequately handle a user's request and then seamlessly transfer the conversation to a team of human experts. This is often called "escalation" or "hand-off." **Steps Involved (Conceptual):** 1. **Detection of Hand-off Need:** The agent needs to determine when it's appropriate to hand off the conversation. 2. **Routing to the Correct Team:** Based on the user's request, the conversation should be routed to the most relevant team of experts. 3. **Context Transfer:** Crucially, the agent needs to pass along the conversation history and any relevant user data to the human agent. This avoids the user having to repeat themselves. 4. **Notification and Availability:** The system needs to notify the available human agents that a new conversation is waiting. 5. **Agent Takeover:** A human agent claims the conversation and takes over from the bot. 6. **Seamless Transition (User Perspective):** The user should experience a smooth transition from interacting with the bot to interacting with a human. 7. **Post-Conversation Analysis:** After the human agent finishes, the conversation data should be analyzed to identify areas where the bot can be improved. **Implementation using a Platform like Microsoft Copilot Studio (Example):** While the exact steps will vary depending on the specific MCP you're using, here's a general outline using Microsoft Copilot Studio as an example: * **1. Identify Scenarios for Hand-off:** * **Low Confidence:** If the bot's natural language understanding (NLU) engine has low confidence in understanding the user's intent, trigger a hand-off. * **Specific Keywords/Phrases:** If the user says things like "I need to talk to a person," "Speak to an agent," or uses specific keywords related to complex issues, trigger a hand-off. * **Escalation Topic:** Create a dedicated "Escalation" topic that users can explicitly trigger. * **Loop Detection:** If the bot gets stuck in a loop (e.g., asking the same question repeatedly), trigger a hand-off. * **Unresolved Intent:** If the bot cannot match the user's input to any known topic after a certain number of attempts, trigger a hand-off. * **2. Configure Hand-off to a Live Agent System:** * **Integration with Live Agent Platforms:** Microsoft Copilot Studio (and similar platforms) typically integrate with popular live agent platforms like: * Microsoft Dynamics 365 Customer Service * Salesforce Service Cloud * Zendesk * Other custom solutions via APIs. * **Configure the Hand-off Topic:** In your Copilot Studio bot, create a topic specifically for handling hand-offs. * **Transfer Conversation:** Within the hand-off topic, use the platform's built-in functionality to transfer the conversation to the configured live agent system. This usually involves selecting the appropriate queue or agent group. * **3. Context Transfer (Critical):** * **Variables:** Before the hand-off, store relevant information from the conversation in variables. This might include: * User's name * User's email address * User's account number * The topic the user was trying to address * The conversation history (if the platform supports it) * **Pass Variables to Live Agent System:** Configure the hand-off action to pass these variables to the live agent system. The live agent system needs to be configured to receive and display this information to the human agent. This is often done through custom fields or attributes in the live agent platform. * **4. Live Agent System Configuration:** * **Queues and Routing:** Set up queues in your live agent system to route conversations to the appropriate teams (e.g., sales, support, technical support). Use the information passed from the bot (e.g., the topic the user was trying to address) to determine the correct queue. * **Agent Notifications:** Configure the live agent system to notify agents when a new conversation is waiting in their queue. * **Agent Workspace:** Ensure that the agent workspace in the live agent system displays the context information passed from the bot. * **5. User Experience:** * **Clear Communication:** When the bot is handing off the conversation, clearly inform the user that they are being transferred to a human agent. Provide an estimated wait time if possible. * **Seamless Transition:** The transition should be as seamless as possible. Avoid requiring the user to re-enter information they have already provided to the bot. * **6. Analytics and Improvement:** * **Monitor Hand-off Rate:** Track the number of conversations that are handed off to human agents. A high hand-off rate may indicate that the bot needs improvement in certain areas. * **Analyze Hand-off Reasons:** Identify the reasons why conversations are being handed off. This can help you prioritize areas for bot improvement. * **Review Transcripts:** Review transcripts of conversations that were handled by human agents to identify areas where the bot could have handled the conversation more effectively. * **Feedback Loop:** Use the insights gained from analytics to improve the bot's knowledge base, NLU, and conversation flows. **Example Scenario (Simplified):** 1. **User:** "I want to cancel my subscription." 2. **Bot:** "I can help with that. What is your account number?" 3. **User:** (Provides account number) 4. **Bot:** "I'm sorry, I'm having trouble processing your request. I'm going to transfer you to a human agent who can assist you." 5. **Bot (Hand-off Topic):** * Stores the user's account number in a variable. * Transfers the conversation to the "Cancellation" queue in the live agent system. * Passes the user's account number to the live agent system. 6. **Live Agent System:** * Notifies an agent in the "Cancellation" queue. * Displays the user's account number to the agent. 7. **Human Agent:** "Hello, I see you'd like to cancel your subscription. I have your account number here. Can you please confirm your name and address?" **Key Considerations:** * **Cost:** Integrating with a live agent system can incur additional costs. * **Agent Availability:** Ensure that you have enough human agents available to handle the volume of hand-offs. * **Training:** Train your human agents on how to handle conversations that have been transferred from the bot. * **Security:** Ensure that sensitive information is handled securely during the hand-off process. * **Compliance:** Comply with all relevant regulations and privacy policies. * **Platform Limitations:** Be aware of the limitations of your chosen MCP and live agent system. **In summary, integrating a team of experts into your agent flow requires careful planning and configuration. By following the steps outlined above, you can create a seamless and efficient experience for your users.** To give you more specific guidance, please tell me: * **Which MCP are you using?** (e.g., Microsoft Copilot Studio, Dialogflow, Amazon Lex, Rasa, etc.) * **Which live agent system are you using (or planning to use)?** (e.g., Dynamics 365 Customer Service, Salesforce Service Cloud, Zendesk, etc.) * **What are the specific scenarios where you want to hand off to a human agent?** With more information, I can provide more tailored instructions.