• Previous Article Insights: An earlier article provided general insights on Model Context Protocol, explaining how MCP acts as a universal adapter for AI assistants to access external systems and bring in useful context for interacting LLMs.
• Current Article Analysis: Continues the analysis and shows how a dedicated MCP server accessing a database enables LLMs to inspect it and offer users useful information using natural language. MCP servers expose three primitives - tools, resources, and prompt templates.
• Use Case: Assumes a user wants to access a database and is interested in database structure, data inside a schema, and business intelligence. SQL queries can be used for the first two categories, but more programming is required for the third. The "distribution" helps achieve this via configuration and natural language.
• Preliminary Set-Up: Used a Windows machine. Set up a PostgreSQL database server with a simple schema called mcpdata and added experimental data. Installed Claude Desktop and connected it to a PostgreSQL MCP Server via configuration.
• "Discussing" With the Database": Analyzed several interactions with the database. Asked for an entity-relationship diagram, details of tables and records, number of paid invoices in June, total amount to be paid, and a visual representation of the invoices table. The AI assistant used the MCP server to gather information and provide accurate responses.
• Conclusion: Demonstrated how a dedicated PostgreSQL MCP Server enriches the context of an AI application and allows users to discover information directly from the database in a natural language conversational manner. SQLite MCP Server is another example, and one can develop their own MCP servers if needed. Resources include various links related to MCP and Claude Desktop.