Cohort definition within the OHDSI ecosystem requires navigating the ATLAS interface or writing code against the WebAPI REST endpoints, imposing a steep technical barrier for clinical researchers without programming experience. This paper presents an MCP server that exposes the OHDSI WebAPI cohort definition subsystem as callable tools, covering vocabulary search, cohort authoring, execution, and reporting, enabling language model clients to construct and persist OHDSI-native cohort definitions through natural language interaction. To evaluate the approach, a small language model running locally on consumer-grade hardware was tasked with replicating a published cohort definition from the public OHDSI ATLAS instance using only natural language instructions. The model successfully reproduced the cohort structure and the majority of its clinical concepts, producing a valid definition that was accepted and stored on a live OHDSI instance in under five minutes, without any prior training on OHDSI workflows. These results demonstrate that well-designed MCP tool interfaces can offset model scale, enabling lightweight local models to execute OHDSI phenotyping workflows that would otherwise demand both domain expertise and significant computational resources.