AgentsMedium impactFor DevGitHub AI Agents · June 10, 2026
On-demand .NET runtime diagnostics over MCP - attach to a live CoreCLR process (sidecar or local), collect counters/CPU samples/exceptions/GC events/dumps, and let an LLM drive performance investigations.
pedrosakuma/dotnet-diagnostics-mcp
A tool that attaches to live .NET CoreCLR processes to collect diagnostics data and leverages an LLM to aid in performance analysis.
Signal strength3.8/5·2 stars
A tool that attaches to live .NET CoreCLR processes to collect diagnostics data and leverages an LLM to aid in performance analysis.
TL;DR
A tool that attaches to live .NET CoreCLR processes to collect diagnostics data and leverages an LLM to aid in performance analysis.
What happened
The repository pedrosakuma/dotnet-diagnostics-mcp provides on-demand runtime diagnostics for .NET CoreCLR processes and integrates an LLM to drive performance investigations based on collected telemetry.
Why it matters
This approach combines low-level runtime diagnostics with AI-driven insights, enabling more efficient and automated performance troubleshooting in .NET applications.
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The bigger picture
This development epitomizes a growing paradigm where AI augments traditional software engineering toolchains by transforming telemetry into actionable intelligence rather than just data points. As observability tools mature, the bottleneck increasingly lies in analysis and reasoning about complex runtime behaviors, a problem well-suited to the pattern recognition and synthesis capabilities inherent in LLMs. By embedding AI agents directly into low-level diagnostic workflows, we are witnessing a strategic shift toward proactive performance management that scales beyond human cognitive limits. This signals broader industry momentum toward AI-native tooling that doesn't just collect logs and metrics but interprets them contextually, reducing mean time to resolution for intricate software systems. The .NET-specific focus also illustrates how vertical integration of AI diagnostics is taking hold in enterprise developer ecosystems, a trend we can expect across platforms.
Technical deep dive
At its core, dotnet-diagnostics-mcp interfaces with the .NET runtime’s Diagnostic IPC Protocol to attach to a live CoreCLR instance, leveraging APIs like EventPipe for tracing and counters, and CLRMD for memory and dump analysis. The design accommodates flexible deployment modes, useful for both local debugging and remote sidecar configurations often found in containerized microservices. Once telemetry is collected, it is transformed into structured diagnostic artifacts that an LLM agent consumes through a prompting abstraction layer, enabling reasoning over event sequences, CPU hotspots, GC inefficiencies, and exception patterns. This approach offloads initial triage from developers by applying AI heuristics, natural language summarization, and hypothesis generation to raw runtime data, effectively acting as a first-line performance analyst. Integrators must consider latency between telemetry capture and AI feedback, as well as security concerns for transmitting diagnostic data. The layered architecture invites extensibility, allowing additional diagnostics streams or custom LLM prompting strategies. Architecturally, the integration underscores a hybrid human-AI decision model for runtime diagnostics, where AI augments but does not replace developer intuition.
Real-world applications
1
A cloud-native .NET service experiencing periodic latency spikes can use this tool to rapidly identify CPU bottlenecks or GC-induced pauses with AI-assisted diagnostics instead of manually parsing traces.
2
DevOps teams can deploy the agent as a sidecar to collect live exception telemetry from production environments and get AI-driven root cause pointers without impacting service uptime.
3
Development teams onboarding performance engineers can leverage LLM-powered insights on collected dumps to accelerate knowledge transfer, reducing reliance on deep .NET runtime expertise.
4
CI/CD pipelines integrating this diagnostic agent can automatically generate performance health reports with AI summaries after load testing, catching regressions before release.
What to do now
Experiment with integrating dotnet-diagnostics-mcp in a non-production environment to familiarize your team with its telemetry collection and AI analysis workflows.
Evaluate current .NET performance debugging bottlenecks in your projects and map how LLM-guided insights could accelerate root cause identification.
Monitor the project's GitHub updates and community feedback to understand evolving best practices and contribute real-world usage scenarios.
Assess security and compliance implications of transmitting diagnostic data to AI components, and design appropriate data governance processes.