"Information about SQL (Structured Query Language)" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Ask your AI about your EventSend events, delivery health and usage.
Ask an agent why a PHP site is slow: every request with its SQL, HTTP calls, errors and N+1.
Uptime and website monitoring for AI agents. Query monitor status, incidents, heartbeats, domain expiry, and status pages in your Vantaj Uptime workspace.
Query analyzed WebRTC sessions: observations, deductions, experience scores, and AI summaries.
GraphQL operation count, query discarded
Query Checkly synthetic monitoring — checks, statuses, results, alerts, reporting and dashboards.
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.
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
Oviond brings data from 100+ marketing platforms into one reporting platform. Through the Oviond MCP server, AI assistants can securely access and work with Oviond clients, projects, reports, dashboards, widgets, and marketing data. Ask questions about your reporting data, analyze marketing performance, and manage reporting workflows directly through your AI assistant.
Analytics for MCP servers. Query your tool calls, first-call success, retries and schema cost.
DevOps, SRE, and QA intelligence for Cursor — investigate bugs, assess release readiness, search logs, triage outages, diagnose failed CI, check deployments, and query connected cloud platforms. Secure remote MCP with OAuth2 and read-only QA subagents.
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
- Session Replay MCPOAuth unavailablecom.session-replay
MCP for retrieving information about recorded session replays.
Connect engineering metrics, DORA performance, and deploy risk scoring to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.
Real-time web analytics for AI agents: query traffic, funnels, revenue, and manage your sites.
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.
- Ionhour MCPOAuth unavailablecom.ionhour.api
Let AI agents monitor and manage your infrastructure through the Model Context Protocol. Query, create, and resolve — all in natural language.
Triage failing GitHub Actions jobs and see what self-heal repaired, in natural language.
The Polar Signals MCP server enables AI assistants to connect directly with performance profiling data, allowing users to analyze application performance through natural language queries. Key capabilities include querying CPU performance and memory usage, exploring profiling metadata like profile types and labels, and providing AI-driven code optimization suggestions directly within development environments like Claude Code or Cursor.
Structured Output MCP Traced Agent
