Multiple AI tools on the same machine can clash over a single local database.
Environment differences (Windows vs Linux, Docker vs VM) limit some operations.
Authentication differs between local and deployed MCP servers.
Generate unique database names per working copy to avoid conflicts.
Use MCP server as a protocol layer instead of direct database access.
Configure HTTP transport and header tokens for secure remote connections.
MCP server enables natural language interaction with applications (e.g., work orders, employees).
Can handle multi-transaction tasks like creating recurring work orders.
Runs in multiple tools and IDEs (VS Code, OpenCode, Claude, GitHub Copilot).
To enable AI tools to process information stored in existing software systems or databases, that data must reach the language model’s context window. There are only two ways to achieve this: (1) include it directly in the prompt, or (2) provide it as the result of a call to an LLM tool/function.
The Model Context Protocol (MCP) offers a standardized pattern for discovering, grouping, and enabling sets of AI tools that language models can access. However, most traditional web services are not well-suited for agentic workflows. To support true agentic patterns with your existing systems, you need an MCP server.
MCP is emerging as the new standard API for large language models.
This training will jumpstart your journey toward designing and implementing an MCP server for your custom system or database.
