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AI Agents Meet MCP Servers to Revolutionize Software Quality
Discover how Parasoft's MCP connects AI agents to structured, standards-aware data from C/C++test, enabling AI agents to automatically fix violations, optimize rule sets, and generate documentation, while keeping engineers fully in control.
Discover how Parasoft's MCP connects AI agents to structured, standards-aware data from C/C++test, enabling AI agents to automatically fix violations, optimize rule sets, and generate documentation, while keeping engineers fully in control.
As artificial intelligence becomes an integral part of modern software development, the next evolution isn’t just about using AI for coding assistance. It’s about creating agentic systems that take meaningful actions to improve software autonomously.
The latest releases of Parasoft C/C++test and C/C++test CT introduce the MCP (Model Context Provider) server. This breakthrough connects AI agents with the deep, structured technical data they need to act intelligently within the development environment.
In safety- and security-critical software, ensuring compliance with coding standards, like MISRA and CERT, and functional safety standards, like ISO 26262, often involves tedious, repetitive work:
Traditionally, engineers handled all these steps manually. With Parasoft’s MCP server, that changes dramatically.
The MCP server exposes key datasets to AI agents allowing them to reason and act on quality issues automatically. These datasets include violation details, static analysis documentation, and code coverage results.
The result?
Unlike generic AI assistants, Parasoft’s MCP server provides structured, standards-aware data. This gives AI agents the context needed to make informed, compliant decisions, bridging the gap between traditional automation and true autonomous quality improvement.
The MCP server transforms how AI agents interact with Parasoft’s testing tools by making rich, context-aware information accessible.
Agents can identify, analyze, and even fix coding rule violations using Parasoft’s extensive database of checkers, explanations, and examples.
In C/C++test CT, agents can examine coverage gaps, generate targeted tests, and apply standardized annotations for uncoverable code—all via MCP access.
Agents leverage Parasoft’s comprehensive tool documentation to guide developers and automate tool interactions. This directly lowers the learning curve and simplifies tool adoption for new users.
With nearly 4,000 static analysis rules, agents can assist users in refining rule sets to target specific vulnerabilities or runtime risks, accelerating scans and improving precision.
Integrating agents with tools and services in the development environment.
Here are just a few of the AI-driven workflows now possible with the MCP server.
AI agents can use the MCP server to retrieve the latest static analysis results and the corresponding rule documentation, including examples of compliant and noncompliant code. With that context, agents can automatically propose or apply fixes that adhere to the intended rule behavior.
This significantly reduces the manual review burden on developers. It also helps maintain compliance integrity without slowing down development cycles.
AI agents analyze the characteristics, relationships, and coverage areas of Parasoft’s extensive rule library—nearly 4,000 checkers—using MCP server data.
They can automatically assemble a rule set optimized for the project’s objectives, such as focusing on cybersecurity vulnerabilities, runtime reliability, or safety-critical requirements.
By identifying overlapping or redundant rules, agents:
All this while ensuring the same level of risk coverage.
In C/C++test CT, AI agents access coverage data via the MCP server to identify untested or partially covered sections of code. Based on this insight, they can generate targeted test cases that exercise those uncovered paths, helping teams reach their desired coverage goals faster and with greater precision. This automation accelerates verification activities and reduces the time spent manually inspecting coverage reports.
Agents detect sections of code that cannot be tested, such as hardware-dependent routines or defensive error paths, and automatically apply standardized annotations and justifications using the MCP server’s annotation templates. This ensures that compliance reports meet regulatory requirements without the manual, repetitive documentation work typically required for safety assessments.
By connecting to Parasoft’s extensive tool documentation and rule database through the MCP server, AI agents can act as intelligent assistants, answering user questions, explaining violations, or guiding developers through configuration and workflow best practices. This reduces onboarding time for new users and ensures consistent tool usage across teams.
By bridging MCP servers with intelligent AI agents, Parasoft is redefining what automated testing means. Rather than static reporting that requires human interpretation, developers now gain a dynamic feedback loop, where AI assists not only in identifying issues but also in resolving them.
This shift from reactive testing to proactive quality management delivers measurable advantages.
This AI-driven workflow transforms compliance from a bottleneck into a continuous, intelligent process so you can deliver safer, higher-quality software faster.
Parasoft’s new approach to agentic AI represents a foundational leap toward intelligent automation in safety-critical software development. By empowering AI agents with rich code analysis data available through MCP servers, Parasoft introduces a new class of intelligent tools capable of offloading a significant portion of compliance-related developer activities. This automation not only saves time but also allows developers to focus on higher-value, creative engineering tasks.
As AI continues to mature, Parasoft’s MCP-enabled ecosystem paves the way for even deeper automation: agent networks that coordinate across tools, perform contextual reasoning, and continuously enhance codebases in real time.
This next generation of AI-augmented testing isn’t just about faster results, it’s about reshaping how teams think about software quality, compliance, and productivity.
Discover how AI-driven workflows help teams like yours achieve continuous compliance to deliver higher-quality code with less effort.
