"Octopus Deploy" matching MCP servers:

Provides agent-native analytics and logging for coding agents, enabling product analytics, error logging, and deploy attribution through an MCP handoff.

AI-powered infrastructure monitoring and management for Claude and other MCP clients, enabling natural language queries for service health, incidents, predictions, root cause analysis, and deploy safety.

Enables users to investigate infrastructure incidents in plain English, correlate observability and deploy data with runbooks, and get evidence-backed root-cause proposals with approval-gated remediation.

Exposes DevOps/SRE operations like Kubernetes pod status, Prometheus metrics queries, and deploy history as tools for MCP-compatible clients.

SuperDev is a local-first desktop app + MCP server that gives AI coding agents (Claude Code, Codex, Cursor — anything MCP-compatible) access to the runtime: live and historical logs across services, breakpoints in already-running processes (same PID, no restart), real browser control, and DAG deploy pipelines — with human approval and an audit trail on protected writes.

Connect engineering metrics, DORA performance, deploy risk scoring, and PR health to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.

Enables AI clients to connect to live IoT deployments, read devices, provision hardware, deploy automation rules, manage alarms, and send commands to physical equipment.

Gives an LLM agent a fixed, typed set of on-call tools over a self-hosted GitOps Kubernetes platform, reading metrics, logs, alerts and deploy history from Prometheus, Loki, Alertmanager and GitHub without any cluster write access. Rollbacks and config changes are proposed as one-shot ids that require explicit human confirmation before becoming pull requests against the Git repository, with both steps recorded in an audit log.

A lightweight MCP server that enables AI assistants to deploy, inspect, and operate Kubernetes workloads through high-level workflow tools, reducing token usage with structured outputs.

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.