"Debugging WebRTC SDK Implementation Issues in Visual Studio 2019" matching MCP servers:

A stateless MCP server for incident lab operations, exposing MCP and health endpoints, with conformance testing.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.

An AI-powered server that provides rapid debugging of server logs with actionable fixes in under 30 seconds, featuring real-time monitoring and root cause analysis through Google Gemini integration.

An MCP server for detecting retry loops and analyzing iteration patterns in agentic coding workflows, providing structured debugging intelligence to improve repair attempts.

Honeycomb MCP (Model-Controller-Presenter) Server implementation

An MCP server implementation for accessing Google Analytics 4 (GA4) data, built using the Model Context Protocol TypeScript SDK.

Connects AI assistants to the Invariance observability platform, enabling natural language queries of traces, monitors, and signals to investigate issues and analyze agent behavior. Supports debugging, monitoring, and evaluation dataset management through Claude Desktop, Cursor, and Claude Code.

Runs, tests, and finds issues in your Python services with zero code changes, then helps your AI agent fix what breaks and proves it with acceptance tests.

Enables AI assistants to retrieve and analyze Sentry issues, including error reports, stacktraces, and debugging information from Sentry.io.

A live post-mortem debugger MCP server for Home Assistant that lets AI agents list captured exceptions, walk traceback frames, read local variables, and optionally evaluate Python in the context of a captured frame for true post-mortem debugging.

Enables querying and managing Sentry projects, issues, and events through natural language.

Enables natural language querying and analysis of OpenTelemetry traces, metrics, and logs stored in Elasticsearch/OpenSearch, allowing AI assistants to investigate performance issues, find root causes, and explore system behavior.

The implementation of Model Context Protocol (MCP) server for VictoriaLogs.

Full read/write MCP server for Bugsink, letting users view and manage teams, projects, issues, events, and releases, including resolving, muting, reopening, commenting on, and deleting issues.

A Model Context Protocol (MCP) server that provides safe, read-only access to Kubernetes resources for debugging and inspection. Built with security in mind, it offers comprehensive cluster visibility without modification capabilities.

The implementation of Model Context Protocol (MCP) server for VictoriaMetrics

MCP server for tracing, logging, and debugging multi-agent systems by capturing sessions, spans, and events in a queryable trace tree.

Enables AI agents to query Microsoft Copilot Studio telemetry and governance data in plain English, pulling from multiple Azure and M365 APIs into a local SQLite database.