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This project serves as a comprehensive example for building a custom Model Context Protocol (MCP) server from scratch. It demonstrates how to integrate an MCP server with a PostgreSQL backend to offer robust task management capabilities, enabling AI assistants to securely connect to external data and tools. The server acts as a bridge, allowing AI models to interact with task data, perform actions, and leverage a JSON-RPC based communication protocol for seamless integration within an AI ecosystem.

Características Principales

• Comprehensive task lifecycle management (create, read, update, delete tasks).
• Ability to execute read-only SQL queries and retrieve database schema information.
• JSON-RPC based communication compliant with the Model Context Protocol standard.
• Access to categorized task resources (pending, in-progress, completed, all tasks).
• Built-in task commenting system for detailed notes and collaboration.
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Casos de Uso

• Serve as a learning resource and template for building custom MCP servers with database integration.
• Allow AI to query and analyze task data, or explore the underlying database schema.
• Enable AI assistants to manage and track tasks, updating statuses and adding comments.