The recent launch of What the Harness? has spotlighted inconsistencies in LLM agent behavior when implementing the Model Context Protocol (MCP). Developers using MCP harnesses are encountering duplicate data outputs, raising questions about the reliability and efficiency of these implementations. This issue is especially pertinent given the increasing reliance on LLM agents for tasks that require precise data handling.
MCP and Structured Data Challenges
The core of the problem lies in how MCP tools handle data outputs. According to Polytomic's field guide, MCP tools can return both text and structured data, but harnesses should ideally pass only one structured payload to avoid duplication. The guide notes, "Our rubric: prefer one structured payload, avoid duplication, preserve single-field results." However, testing has revealed that many harness implementations, including those using ChatGPT and Claude, fail to adhere to this standard, resulting in duplicate outputs. This duplication can flood models with unnecessary tokens, potentially affecting performance and accuracy.
GitHub Copilot's Approach
In contrast, the GitHub Copilot Harness offers a more structured approach to building LLM agents. Integrated with the Microsoft Agent Framework, GitHub Copilot enables developers to create agents that reason about code, modify files, and execute commands with greater control and observability. The integration allows for a consistent execution surface, leveraging Copilot's capabilities in shell execution, file manipulation, and MCP tool usage within a broader agent framework.
The Copilot's agentic harness acts as an execution engine, managing model calls and planning while providing middleware, observability, and human-in-the-loop features. This integration aims to give developers the ability to build production-ready agents with reliable data handling capabilities, potentially mitigating the issues observed in other MCP implementations.
The Tension Between Complexity and Control
The inconsistencies highlighted by Polytomic's tests and the structured approach offered by GitHub Copilot underscore a central tension in AI development: managing the complexity of LLM agents while maintaining control over their outputs. As developers seek to leverage LLMs for more sophisticated tasks, the need for reliable and consistent data handling becomes increasingly critical. The challenge lies in balancing the flexibility and power of LLMs with the precision and control required for production environments.
A Call for Standardization
The issues faced by MCP harnesses and the solutions proposed by GitHub Copilot suggest a need for greater standardization in LLM agent implementations. Ensuring consistent outputs and avoiding data duplication are essential for maximizing the utility and efficiency of LLM agents. As developers continue to explore the potential of these tools, establishing clear standards and best practices will be crucial for their successful integration into production workflows.