Overview: The MCP server acts as middleware, enabling AI agents to automate tasks by connecting to local data and remote services. It supports functions such as query execution and prompting to efficiently handle context. This represents a step towards fully automated AI systems capable of performing complex operations.

This project implements an MCP service based on Azure Functions, serving as a bridge between AI agents and the AlphaVantage financial API, providing access to financial data such as company overviews and financial statements.

Overview: MCP is a standard protocol, similar to the Type C protocol of electronic devices (which can both charge and transmit data), enabling AI models to interact with different APIs and data sources. It aims to replace fragmented Agent code integration and improve the reliability and efficiency of AI systems. By establishing a common standard, service providers can offer their own AI capabilities according to the protocol, supporting developers to build more powerful AI applications more quickly. MCP maintains the context between different applications and services, enhancing the overall ability of autonomous task execution.

Overview: The MCP server is an intermediate layer that informs the AI agent of the currently available services, APIs, and data sources. It enables the AI agent to decide whether to call a certain service based on the information provided by the Server and execute functions through Function Calling.

Overview: The Model Context Protocol (MCP) is a protocol designed to enable AI agents to access and utilize context, tools, and prompts. It allows for the creation of servers that provide standardized interfaces to interact with local or remote services, thereby enabling automated workflows such as reading files, operating databases, debugging code, and pushing changes to GitHub.

Overview: The MCP Server aims to achieve full automation of AI agents. By providing a standardized protocol as an intermediate layer, it informs AI agents about currently available services, APIs, and data sources, enabling them to decide whether to call specific functions to perform tasks such as code modification, repository search on GitHub, problem search and creation, etc.

Overview: MCP (Model Context Protocol) is a framework for AI automation through a standardized protocol, which includes key components such as hosts, clients, servers, local data sources, and remote services. The core lies in the server, which provides context, tools, and prompts for clients. MCP enables AI agents to automate tasks, such as code generation, debugging, and interaction with external services like GitHub repositories.

Overview: The README.md file provides an overview of the Model Context Protocol (MCP) Server, detailing its components and functions. It explains how MCP enables AI automation through a standardized protocol and connects hosts, clients, servers, local data sources, and remote services. The document also describes the evolution from basic AI chats to advanced AI agents capable of performing automated tasks.