How to Build MCP Servers and AI Agents in Python

TL;DR

MCP standardizes how an LLM application connects to external services, allowing an MCP client to discover and use tools exposed by MCP servers. With the Python SDK, developers can build clients and servers, connect them through standard transports, integrate third-party services, add LLM-driven tool selection, create application interfaces, and prepare the resulting agentic AI system for deployment with Docker.

Transcript

Hello all, my name is Krishna and welcome to my YouTube channel. So guys, welcome to this short crash course on MCP that is model context protocol. In this specific video, we are going to cover many topics. I will be talking about the agenda but everything that has come till now related to MCP I will be covering in this specific video from explaini... Read More

Key Insights

• MCP is a standardized protocol that lets an LLM application communicate with external services through an MCP client and one or more MCP servers, separating the work of supplying context from the model interaction itself.
• An MCP server is responsible for exposing tools, resources, and prompts while connecting to underlying services such as local data sources, remote databases, company APIs, or remote APIs available through the internet.
• An MCP client is the application-side component that uses the MCP protocol to communicate with servers, allowing hosts such as Claude Desktop, Cursor, VS Code, Streamlit applications, or FastAPI applications to access server capabilities.
• Service-provider management reduces application-side maintenance because the provider handles its server and the communication with its underlying service, while the developer focuses on creating a compatible MCP client and using the standardized protocol.
• Tool discovery occurs before model selection because the host first contacts MCP servers to obtain information about their available tools, then supplies the user's input and those tool definitions to the LLM for a decision.
• Tool descriptions help the LLM choose an appropriate service because information such as a tool's docstring communicates its purpose, enabling the model to relate a user request to a capability exposed by an MCP server.
• The Python SDK supports building MCP clients, creating servers that expose resources, prompts, and tools, handling protocol messages and lifecycle events, and using standard transports such as stdio and SSE.
• Docker setup prepares an MCP-based agentic AI application for deployment, allowing the same packaged system to be deployed to an EC2 instance, AWS, Azure, or another selected environment after development and integration are complete.

Questions & Answers

Q: What is the Model Context Protocol used for?

The Model Context Protocol is used to standardize communication between an LLM application and external sources of context or functionality. An MCP client inside the host communicates through the protocol with one or more MCP servers. Those servers can expose tools, resources, and prompts while connecting to local data, remote databases, company APIs, or other internet-based services.

Q: How does an MCP client communicate with an MCP server?

An MCP client communicates with a server through the MCP protocol. The client belongs to a host application, which may be Claude Desktop, Cursor, VS Code, Streamlit, FastAPI, or another application. The server exposes its available capabilities and communicates with underlying services. This arrangement lets the host access external context without implementing a separate integration pattern for every service.

Q: How does an LLM select the correct MCP tool?

The host first contacts the connected MCP servers and receives information about the tools they expose. It then provides the user's input and the available tool information to the LLM. Descriptions associated with the tools, including docstrings, help the model understand each capability and decide which server or service is appropriate for the specific request.

Q: How can you build an MCP server with Python?

An MCP server can be created with the Python SDK by defining the tools, resources, or prompts that the server should expose. Tool descriptions should clearly state what each capability does so an LLM can interpret them. After the server is created, it can be run and inspected through MCP Inspector or connected to supported desktop and development hosts.

Q: How can an MCP server be tested and run?

An MCP server can be examined with MCP Inspector and run through hosts such as Claude Desktop, Cursor, or VS Code. These environments act as hosts with MCP clients that connect to the server through the protocol. Testing should confirm that the host can discover the server's tools and provide their definitions to an LLM alongside the user's request.

Q: How can third-party MCP servers be integrated?

Third-party MCP servers can be connected directly to compatible hosts such as Claude Desktop or Cursor. Once configured, the host's MCP client communicates with those servers through the standard protocol and retrieves their available tools. The LLM can then use the tool descriptions and user input to determine which third-party service should handle a particular task.

Q: How can MCP be added to a Streamlit or FastAPI application?

MCP can be added by implementing an MCP client within a Streamlit, FastAPI, or other application. The client connects to one or more MCP servers and retrieves the context or tools they provide. That information is then integrated with an LLM so the application can create agents, select relevant services, and implement task-specific solutions for user requests.

Q: Why is Docker useful for an MCP agentic AI application?

Docker setup is useful because it prepares the completed MCP application for deployment after the client, servers, services, and LLM have been integrated. The packaged setup can then be deployed to an EC2 instance, AWS, Azure, or another chosen environment. This makes container configuration an important final stage of the development workflow described in the course.

Summary & Key Takeaways

• MCP separates external context and service integration from direct LLM interaction. An application uses an MCP client and the protocol to communicate with one or more MCP servers. Those servers expose tools, resources, or prompts and may connect to local data, remote databases, company APIs, or internet-based services.

• The practical workflow begins by creating an MCP server with the Python SDK and defining tools with useful descriptions. The server can then be examined through MCP Inspector and connected to hosts such as Claude Desktop, Cursor, or VS Code. Existing third-party MCP servers can also be integrated into these environments.

• An MCP client can be incorporated into a Streamlit, FastAPI, or other application and connected with an LLM. The host discovers the server's available tools, sends their information with the user's input to the model, and lets the model determine which capability is relevant. Docker configuration supports subsequent cloud deployment.