MCP & Claude Skills: Using Both to Build Agentic Workflows

CData’s comparison of Claude Skills and the Model Context Protocol (MCP) argues that the two approaches serve complementary roles rather than replacing one another. MCP enables AI agents to discover and explore external systems, schemas, tools, and data sources, while Claude Skills package known, repeatable workflows into instructions and code that can execute tasks with less model context. In controlled tests using Salesforce and Zendesk data through CData Connect AI, MCP combined with Skills reduced token use by 65% for data discovery, 34% for a simple revenue query, and 58% for a cross-system join compared with MCP alone. The savings resulted from using MCP initially to identify available data and construct requests, then moving established requests into Skills that call APIs directly and return streamlined results rather than requiring the model to process extensive schema metadata and verbose JSON. The proposed workflow is therefore to use MCP for initial discovery and understanding, then create Skills for efficient, reliable execution of recurring tasks.

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