How Kimputing &
CenterTest use AI
Deterministic testing you can trust. AI-powered solutions when
you're ready.
​CenterTest executes tests as consistent, repeatable code. Separately, your teams can securely deploy AI-powered solutions at their own pace, and without embedding AI into test execution.
Locally-deployed
MCP Architecture
Model Context Protocol (MCP) is an open standard
that allows AI assistants to drive maximum business value while minimizing security risks.
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The MCP servers recommended by Kimputing are deployed locally, and enable AI to provide context-driven support, maintenance, and custom analytics.
Together with tooling controls, branch isolation, and directory scoping, MCPs help you capitalize on modern AI solutions without compromising security.
MCPs for CenterTest
CenterTest Documentation MCP
Provided by Kimputing​
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The CenterTest Documentation MCP provides AI assistants access to framework documentation, configuration references, troubleshooting guides, and known error patterns.
Users can resolve common support questions through natural conversation, including:
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Configuration guidance: Property settings, runtime options, environment configuration, and execution parameters.
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Framework patterns: Correct usage for restart points, multi-user scenarios, data-driven structures, and reusable extensions.
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Stack trace analysis: Exception interpretation, error code explanation, root cause identification, and resolution steps.
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Troubleshooting support: Diagnostic approaches for common issues, environmental problems, and unexpected behavior.
Questions that previously required documentation searches or support requests can instead be resolved in seconds with AI. Stack trace analysis is particularly valuable to test debugging, providing documented failure causes and remediation steps that AI assistants can interpret and explain contextually.
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The Kimputing
AI Philosophy
Test automation exists to provide consistent validation. AI tools should be an enhancement, not a dependency.
AI assistance accelerates automation development, simplifies support, and enhances analytics.
By deploying AI-assisted development and support tools, organizations gain additional automation velocity without sacrificing test execution reliability.
