"Software or tools developed using Java" matching MCP connectors:

Diagnose AI workflows for failure, security, and handoff risks — RED/AMBER/GREEN per node.

Live reliability for AI agent tools: is it working right now, and how do I call it correctly?

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

Live status, API pricing and rate limits for ChatGPT, Claude, Gemini, Cursor and 42+ AI tools.

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.

ResilienceOracle - 10 operational resilience tools: BIA, RTO/RPO, scenario testing.

DriftOracle - 15 tools for model/data drift monitoring: PSI, KS-test, alerts, evidence packs.

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.

Capture, inspect & debug HTTPS traffic across iOS, Android, browsers & backends — 304 MCP tools.

Uptime, SSL, DNS and domain monitoring you can talk to from Claude or any MCP client.

Free MCP tools: the only MCP linter, health checks, cost estimation, and trust evaluation.

Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.

Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.

Read-only MCP access to a documented IT fleet: state, changes, posture. 15 tools.

Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.

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.

Data observability tools for engineering teams: alerts, freshness, schema drift, lineage, quality.

AI visibility monitoring. 24 tools, 8 LLM platforms, Hallucination Guard. Free tier.

Debug production issues using Shipbook logs and Loglytics error insights.