Como Usar MCP Para Criar Agentes iA Avançados Incríveis em Python no Cursor e no Claude da Anthropic

Summary

In this video, the speaker introduces the Model Contact Protocol (MCP), a tool that enables AI agents to seamlessly communicate with multiple services, making them more autonomous. Through a hands-on Python code demonstration, the speaker shows how MCP allows agents to decide which tools to use based on task descriptions, such as searching the web. While MCP simplifies tool integration, concerns about security, complexity, and public use arise. The video encourages experimenting with MCP locally to unlock its potential for creating autonomous agents that can perform tasks independently, offering flexibility and efficiency.

Takeaways

• 😀 MCP (Model Contact Protocol) is a new way for AI agents to communicate with various tools, aiming to improve how the internet interacts with AI by simplifying API integration.
• 😀 The MCP protocol emerged in November 2024, but it gained significant attention recently due to its potential for building intelligent agents with more flexibility in accessing tools.
• 😀 One of the key benefits of MCP is reducing the complexity of managing many rigid API integrations for tools like Google, LinkedIn, WhatsApp, etc., by allowing agents to make decisions about when and how to use each tool.
• 😀 The script demonstrates how to set up a simple Python program to make use of the MCP protocol, which allows an AI agent to search the internet using Duck Duck Go.
• 😀 To use MCP, the script requires the installation of two packages: 'MCP' for the protocol and 'Duck Duck Go search' for the search functionality.
• 😀 The agent interacts with a tool defined in the code (Duck Duck Go search) and makes decisions based on the input parameters, such as requiring the query to start with 'please' for a valid response.
• 😀 When the agent conducts a search, it uses the MCP tool to communicate with Duck Duck Go and returns results, demonstrating a local communication model rather than a cloud-based one.
• 😀 The integration of MCP with the Cursor platform allows agents to use local tools to perform actions such as searching the internet, and they can be configured with various commands for local or online communication.
• 😀 Security concerns were raised about the MCP protocol, such as the lack of password protection and the potential for unauthorized use if the link to the server is exposed.
• 😀 While MCP is an excellent tool for local use and experimentation with AI agents, it may not be ideal for professional or public-facing services due to the technical challenges and limitations in scalability and security.

Q & A

What is MCP (Model Contact Protocol) and why has it gained attention recently?

MCP is a protocol designed to allow Artificial Intelligence agents to communicate with various tools they use. It has gained attention because it simplifies API integrations, enabling agents to autonomously decide which tools to use based on a task, such as searching the web, sending messages, or checking emails. This flexibility attracts those developing intelligent agents who want to make API usage easier.

What problem does MCP aim to solve in the context of AI agents and API integrations?

MCP solves the issue of managing multiple, rigid API integrations. Instead of having to configure different APIs for each service (like LinkedIn, WhatsApp, etc.), MCP allows an AI agent to automatically select the appropriate tool for a given task from a list of available tools.

What are the steps involved in setting up the code to test MCP on a local machine?

To set up and test MCP, you first need to install the necessary Python packages using `pip install MCP` and `pip install DuckDuckGo-search`. Then, you create a server using Fast MCP and configure it with tools such as DuckDuckGo search. After setting up the MCP server, you can use it within a local environment for testing without requiring online access.

What is the role of DuckDuckGo search in the example code provided?

DuckDuckGo search is used in the example code as the tool that the AI agent will utilize to search the internet. The code implements a custom search function, and the AI agent interacts with this function to retrieve search results.

How does the AI agent interact with the MCP server to perform tasks?

The AI agent interacts with the MCP server by invoking tools that are described in the MCP configuration. When the agent is tasked with a job, it checks which tools are available and then uses the appropriate one based on the task at hand. In the provided example, the agent is tasked with searching the web and uses the DuckDuckGo search tool.

What does the 'Please' keyword in the script signify and how does it affect the agent's response?

The 'Please' keyword is a part of the agent’s interaction rules. If a query doesn't start with 'please,' the agent returns an error message, emphasizing politeness. This design reflects an educational approach where the agent expects certain etiquette in user requests.

What challenges or concerns are associated with using MCP in a public environment?

One concern with using MCP publicly is the lack of security. Once the server is running, anyone with access to the link can use it, which poses potential risks. Additionally, there is no marketplace or authentication system for verifying the integrity of the tools. Some also worry about the server needing to be constantly active, which may be costly and unnecessary for smaller-scale users.

Why is MCP not recommended for professional, open-use applications?

MCP is not recommended for professional, open-use applications because it lacks security features like authentication or access control. It is more suited for local, personal use, where the user can ensure its security. Additionally, if too many tools are added, the agent can become disorganized and lose its effectiveness.

What is the benefit of using MCP locally on a personal machine?

Using MCP locally on a personal machine allows for greater control over the AI agent’s capabilities without relying on cloud services. The user can create custom tools, configure agent tasks, and let the agent autonomously perform actions based on the tools it has, all while keeping everything private and secure.

What are the potential use cases for MCP in developing autonomous agents?

MCP is ideal for creating autonomous agents that can handle various tasks, such as internet searches, communication via messaging platforms, or interacting with different services. Its flexibility allows agents to decide on the right tool for the job, enabling more efficient automation of tasks that would otherwise require manual intervention.