"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.

agent-bom is an open security scanner and self-hosted control plane for AI, MCP, and cloud infrastructure. The default scan profile in MCP server mode exposes 8 MCP tools. Additional profiles provide inventory, findings, compliance, graph, and runtime workflows. See the published Glama release for its packaged version.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

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.

Provides read-only production observability through a small set of tools for health, job status, latest data, approved logs, and deployment metadata, enabling AI planning and review without a production write path.

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.

Enables read-only, stack-aware diagnostics for microservices on Kubernetes, turning debugging runbooks into conversational tools that investigate slow requests, changes, leaks, and in-pod conditions without exposing cluster credentials.

MCP server allowing AI agents to query New Relic for debugging incidents.

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 Pi to connect to Sentry's MCP endpoint, allowing an agent to search issues, events, and traces after OAuth sign-in.

Records runtime events from any app via zero-code instrumentation and exposes them to LLMs through MCP for autonomous debugging.

A local stdio MCP server with recording and tracking tools that lets Codex and Claude Code agents log chat activity, tasks, and delegations onto a loopback-only visual Kanban board with animated mascots. It enables observing work in progress while leaving conversation and execution in the originating client, without hosting models or running AI itself.

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 tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.

Read-only MCP server exposing Flowsery analytics and AI-detected issues via 17 GET tools, enabling natural language queries in compatible clients.

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.

Enables interaction with Sentry organizations, projects, and issues directly from MCP clients like Claude Code.

A Model Context Protocol (MCP) server implementation for Humio, enabling integration with Humio's logging and analytics platform.

Streams browser DevTools console, network, storage, and performance data to your IDE's AI agent for real-time debugging and analysis.

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.