Separating Intelligence from Execution: A Workflow Engine for the Model Context Protocol
Large Language Model (LLM) agents increasingly interact with external systems through tool-calling protocols such as the Model Context Protocol (MCP). In prevailing architectures, the agent must reason about every tool invocation in every session, consuming tokens proportional to the number of actions performed--even when the task has been solved before. We present the MCP Workflow Engine, a novel MCP-native orchestration layer that decouples intelligence (deciding what to do) from execution (carrying it out). An agent reasons once to produce a declarative workflow blueprint--a JSON document specifying a directed sequence of MCP tool calls with parameterized templates, loops, parallel bra...
| Authors | Abhinav Singh Parmar |
|---|---|
| Primary category | cs.DC |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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