"Debugging WebRTC SDK Implementation Issues in Visual Studio 2019" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Stateful WebSocket session registry with per-connection Shannon entropy delta tracking for schema di
Query analyzed WebRTC sessions: observations, deductions, experience scores, and AI summaries.
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
Uptime and website monitoring for AI agents. Query monitor status, incidents, heartbeats, domain expiry, and status pages in your Vantaj Uptime workspace.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
Proxy Gemini (Vertex AI) completions wrapped in OpenTelemetry trace spans; returns the answer plus t
Mezmo MCP is a remote Model Context Protocol (MCP) server that lets AI assistants and IDE chat agents interact with the Mezmo observability platform via the Model Context Protocol. Use it for streamlined observability, log analysis, and root-cause analysis in your favorite tools. Add Mezmo MCP and you can: 🕵️ Run advanced Root-cause analysis over recent logs 📦 List and describe Pipelines 📤 Export and filter Logs with powerful query syntax
Governance and grounding layer for engineering teams running AI coding agents (Claude Code, Cursor, Codex). Grounds agents in your codebase's knowledge graph, and adds session audit, policy controls and cost/token visibility.
Analyze web performance and get optimization insights from GTmetrix, directly in your AI workflow.
Interact with a global network measurement platform.Run network commands from any point in the world
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
The official MCP server for Lizard (lizard.build). Ship a service, add Managed Postgres, Managed Redis or Managed Object Storage, read logs and metrics, set secrets, scale replicas and attach domains — in plain words. 33 tools over Streamable HTTP, OAuth 2.1; destructive tools require an explicit confirmation argument.
Discover software problems, analyze evidence, and create implementation-ready Build plans.
Connect AI assistants to AppAmbit — the command center for your mobile & desktop apps. Query real-time analytics, sessions, and crash reports; read and push remote config; send push notifications, provision and query managed per-app SQLite databases, deploy serverless Cloud Code functions; and manage a headless CMS. Also generates SDK setup snippets and runs integration diagnostics. Supports .NET MAUI, Swift, Objective-C, Android and more. Built for indie devs, mobile teams, and agencies.
Analytics and debugging for your MCP server — explore usage and sessions, then root-cause errors.
Check infrastructure health, manage incidents, and run runbooks in Faultline.
Read-only Yandex Metrika MCP. Query visits, sources, geo, devices and more in plain language.
Register every AI agent, log every action, prove it. EU AI Act compliance built in.
MCP-native AI SRE: ask what's broken in production, get a reviewed GitHub fix PR.
MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.
