Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)

Fuente: arXiv
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Autores principales: Koc, Vincent, Verre, Jacques, Blank, Douglas, Morgan, Abigail
Formato: Preprint
Publicado: 2025
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author Koc, Vincent
Verre, Jacques
Blank, Douglas
Morgan, Abigail
author_facet Koc, Vincent
Verre, Jacques
Blank, Douglas
Morgan, Abigail
contents AI development environments are evolving into observability first platforms that integrate real time telemetry, prompt traces, and evaluation feedback into the developer workflow. This paper introduces telemetry aware integrated development environments (IDEs) enabled by the Model Context Protocol (MCP), a system that connects IDEs with prompt metrics, trace logs, and versioned control for real time refinement. We present design patterns for local prompt iteration, CI based optimization, and autonomous agents that adapt behavior using telemetry. Rather than focusing on a single algorithm, we describe an architecture that supports integration with frameworks like DSPy, PromptWizard, and Prompts as Programs. We demonstrate this through Opik, an open source MCP server for LLM telemetry, and position our approach within the emerging LLMOps ecosystem. This work lays a foundation for future research on prompt optimization, IDE agent tooling, and empirical benchmarking in telemetry rich AI development workflows.
format Preprint
id arxiv_https___arxiv_org_abs_2506_11019
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)
Koc, Vincent
Verre, Jacques
Blank, Douglas
Morgan, Abigail
Software Engineering
68T01, 68N30
I.2.6; D.2.7
AI development environments are evolving into observability first platforms that integrate real time telemetry, prompt traces, and evaluation feedback into the developer workflow. This paper introduces telemetry aware integrated development environments (IDEs) enabled by the Model Context Protocol (MCP), a system that connects IDEs with prompt metrics, trace logs, and versioned control for real time refinement. We present design patterns for local prompt iteration, CI based optimization, and autonomous agents that adapt behavior using telemetry. Rather than focusing on a single algorithm, we describe an architecture that supports integration with frameworks like DSPy, PromptWizard, and Prompts as Programs. We demonstrate this through Opik, an open source MCP server for LLM telemetry, and position our approach within the emerging LLMOps ecosystem. This work lays a foundation for future research on prompt optimization, IDE agent tooling, and empirical benchmarking in telemetry rich AI development workflows.
title Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)
topic Software Engineering
68T01, 68N30
I.2.6; D.2.7
url https://arxiv.org/abs/2506.11019