Chaining MCP Tools: Build AI Workflows That Search, Read, Analyze, and Write

What is MCP Tool Chaining? Imagine an AI that can not only understand a request like "Analyze our codebase for security vulnerabilities and report them," but also execute that request end-to-end. This requires more than just a single AI model. It needs an orchestration layer that allows the AI to: Search external systems (e.g., GitHub, a file system). Read and comprehend various data formats (code, documents, database records). Analyze the information using its inherent intelligence. Act on its findings by writing code, creating issues, generating reports, or sending messages. MCP tool chaining is the mechanism that makes this possible. It's an architecture where AI models interact with a standardized set of tools (MCP servers) that expose real-world capabilities. When an AI needs to perform a task that requires external interaction, it invokes the appropriate tool, processes the output, and then uses another tool to continue the workflow. Example Workflow 1: Code Analysis and Issue Cr