"MiniMax MCP (Model Context Protocol) GitHub Repository" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Connect AI assistants to PingRelay to monitor websites and APIs, investigate incidents, retrieve uptime and response-time metrics, manage maintenance windows, and access status pages through the Model Context Protocol. Access: Requires a PingRelay paid plan with MCP access and an authorized API key.
Vigilia watches your live app, explains every problem in plain language and writes the fix prompt. Your AI fixes it; Vigilia confirms with real traffic. Vigilia watches web apps that are live with real users, including apps built with Lovable, Bolt, v0, Cursor and Claude Code. It catches errors, failed requests and downtime, explains each problem in plain language, and writes a fix prompt for it. Connect Vigilia to Claude and ask what's broken. Claude lists your open incidents worst first. For each one it reads the plain-language diagnosis, who it affects, the technical evidence (error, page, failing request, stack frames and what the visitor did right before) and Vigilia's fix prompt. In Claude Code or another coding agent, Claude can then fix the cause in your own codebase. Once you've deployed, it marks the incident fixed, and Vigilia watches real traffic to confirm the error stayed away. If it comes back, the incident reopens as a regression. You can also ask how an app is doing (uptime, open incidents, today's traffic), get the exact install snippet for a new project, and check that a live site is running Vigilia. Vigilia never reads or changes your code: the fixing happens in your session, by your AI, in front of you. The only tool that changes anything is marking an incident fixed (the install check just records its result on the app), and you can connect with read-only access. Your data is stored in the EU. Emails, tokens and card numbers are stripped from what visitors' browsers send, and only that scrubbed error context is sent to Anthropic to write the diagnosis. Requires a Vigilia account. The free plan covers one app; reporting backend errors (Supabase, Next.js routes, Workers) is part of Pro.
Project context, backlog tasks, and controlled workflows for coding agents. OAuth sign-in.
Production errors your coding agent can read: search, stack traces, context, trends, resolve.
Deploy apps from GitHub, read build/runtime logs, and manage your app's users from your AI agent.
Explore GitHub Actions job performance, compute costs and failures with Runzumi analytics.
Is GitHub, npm, Cloudflare or your AI provider down right now? 20 status pages, one call.
Cost per run, tool, model and day for AI agents, from one call or an OpenTelemetry export.
Observed facts on public MCP servers: protocol checks, tool changes, signed evidence. No verdicts.
Search + patterns, maturity assessment, context pricing, redaction checks - a practice as 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.
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.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
DriftOracle - 15 tools for model/data drift monitoring: PSI, KS-test, alerts, evidence packs.
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.
MCP-native AI SRE: ask what's broken in production, get a reviewed GitHub fix PR.
Correlate GitHub releases with Sentry issues and PagerDuty incidents.
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
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.
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
StatusNow provides native Model Context Protocol (MCP) access to query website uptime, open ports, security header vulnerabilities, and log traces. Enable your AI assistant to check endpoint health, audit infrastructure security, and trigger alerts—all through a unified, zero-card free tier.
Read your Golem fleet's ledger from any MCP client: agents' sessions, live activity, signed record.
Let AI agents monitor and manage your infrastructure through the Model Context Protocol. Query, create, and resolve — all in natural language.
Brazilian hosting operated end to end by the coding agent: create an app, provision PostgreSQL, Redis and object storage, deploy from GitHub Actions, read container logs, measure memory and CPU, and see the cost in BRL before spending it. Irreversible operations require a human approval code that the agent never sees. Prepaid credit, paid by Pix.
Triage failing GitHub Actions jobs and see what self-heal repaired, in natural language.
The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.
AI agent observability for production traces, natural-language insights, and improvement loops.
Data + AI observability — monitor and troubleshoot production-grade agents and the context they use.
Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.
