Bridging AI and Enterprise: A Model Context Protocol Implementation for Unified Workplace Productivity | IJCT Volume 12 – Issue 6 | IJCT-V12I6P51
International Journal of Computer Techniques
ISSN 2394-2231
Author
Amit Gupta
Abstract
As enterprise software stacks have grown, so has the burden on knowledge workers. Engineers and analysts now spend a surprising amount of their day just navigating between different platforms—switching from docs to source control to issue boards to observability tools—rather than doing actual work. This paper represents a novel implementation of the Model Context Protocol (MCP) that bridges Large Language Models (LLMs) with enterprise services to create a unified AI-powered assistant. We built our system to connect with the tools teams already use every day—Confluence for documentation, GitLab for code, Jira for tracking projects, and monitoring platforms like Grafana, OpenSearch, and Open Telemetry. By using a common protocol to tie everything together, users can simply ask questions in plain English and get answers from any of these systems. Our research examines real-world productivity changes across three key areas: building software, handling incidents, and writing technical docs. When we measured the outcomes, teams found the information they needed in roughly half the time, while their ability to work across different systems improved by over a third. The implementation validates MCP as a viable standard for enterprise AI integration while providing actionable guidance for organizations aiming to improve how they work using generative AI.
Keywords
Model Context Protocol, Large Language Models, Enterprise Integration, AI Assistants, DevOps, Observability, GitLab, Jira, Confluence, Grafana, OpenSearch, Workplace Productivity.
Conclusion
The Model Context Protocol’s production execution for enterprise AI integration is described in this paper. Our architecture seamlessly links multiple tracking platforms such as Confluence, GitLab, Jira, Grafana, OpenSearch, as well as Large Language Models to build an integrated natural language interface for productivity at work. Significant advantages were shown in the case study, including a 97.9% tool invocation success rate, an 81.4% average reduction in task completion time (p<0.001), and high user satisfaction (4.5/5.0 average rating). These findings support MCP as a workable standard for enterprise AI deployments and indicate that careful integration design can lead to significant productivity gains. Key contributions of this work include:
- A modular, production-ready architecture for multi-service MCP integration
- Quantitative evidence for productivity improvements across common DevOps workflows
- Practical optimization strategies reducing token consumption by 84.7%
- Design patterns and lessons learned applicable to other enterprise contexts
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How to Cite This Paper
Amit Gupta (2025). Bridging AI and Enterprise: A Model Context Protocol Implementation for Unified Workplace Productivity. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
Bridging AI and Enterprise A Model Context Protocol Implementation for Unified Workplace ProductivityDownload
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