"How to find people on LinkedIn using their username" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Fixter's MCP provides a stream-lined agentic way to onboard, setup and use the Fixter monitoring and observability platform. Check out more at https://fixter.dev/
Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.
Real-time status & uptime monitoring for 294 popular APIs — is it down, and how reliable?
AI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.
Read-only WooCommerce checkout and revenue incident diagnosis using privacy-safe store signals and public release evidence.
Observed facts on public MCP servers: protocol checks, tool changes, signed evidence. No verdicts.
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.
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke. One connection covers every site on the account.
Observatory operated and funded by devlo: real tools on frozen tasks; intervals, cost, limits.
MCP tool observatory: do registry servers answer, and are their answers true? No key.
Live status for 172 cloud and SaaS vendors from their official feeds. Is it you, or is it them?
Report-To group count, body discarded
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
Uptime, API and server monitoring with outages, reporting, on-call and status pages.
Read-only MCP access to sessions, funnels, campaigns, errors, live visitors, and anomalies.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
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.
Analytics for MCP servers. Find out which of your tools agents get wrong. MCPulse shows you which tools AI agents retry, which come back empty, and which they never call at all. Two lines inside your own server. It never sees your arguments or your results. getmcpulse.com
