"Git MCP (Model Context Protocol) Server" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
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.
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.
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.
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.
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.
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
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
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.
Jam recordings (video, console, network, logs) as context for your coding agent.
Read your Golem fleet's ledger from any MCP client: agents' sessions, live activity, signed record.
- 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.
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.
Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.
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.
