"An overview or resources on Kubernetes" matching MCP servers:

An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.

Enables ChatGPT or other MCP clients to control native Codex sessions on Windows, providing tools for thread management, turns, observation, steering, and responding to approvals.

Exposes 31 high-assurance agentic coding skills as MCP tools and resources, enabling AI agents to invoke structured workflows across DISCIPLINE, TECHNIQUE, KNOWLEDGE LAYER, and REFERENCE registers.

Provides per-Subagent MCP controls to any coding agent or client across all your MCPs and prevents context window waste. Loads only 3 tools instead of all your MCP Server's tool definitions. Agents discover tools on-demand, only when needed and only the servers and tools they are allowed.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

Drive Elixir terminal UIs headlessly from an agent: start a TUI session, send keystrokes, take structured screenshots, and read TEA model state as MCP resources. Also exposes adaptive-UI recommendation tools and, when raxol_agent is loaded, read-only coding-agent session tools.

An MCP server that lets coding agents scaffold, run, and iterate on Maltego transforms against a live local server, reading back entity results to validate and improve mappings.

Connects AI coding agents to the SkillsMP marketplace, allowing users to search, read, and install over 8,000 community-made skills. It enables agents to gain new capabilities either through on-the-spot instruction or permanent installation without requiring an API key.

Provides coding agents with repository context through tools that generate AGENTS.md files or structured JSON summaries, quoting build/test commands from the repo's manifests for a small fee without an API key.

An MCP server that connects Gemini 2.5 Pro to Claude Code, enabling users to generate detailed implementation plans based on their codebase and receive feedback on code changes.

Enables Claude or any MCP client to drive the GitHub Copilot cloud agent via its REST API, allowing it to research, edit, and open pull requests on repositories without manual intervention.

An MCP server that allows coding agents to look up contextual rules and patterns on demand, providing just-in-time guidance for specific tasks like writing tests or authoring UI.

An MCP server that lets AI coding agents work on Godot 4 projects, including reading and editing scenes, writing and validating GDScript, running the game, and capturing screenshots.

An MCP server that enables AI clients to autonomously discover, analyze, fix, test, submit, and track open-source contributions on GitHub through specialized tools.

Delegates heavy development tasks from a supervisor to an autonomous worker on a cheaper model via MCP, with isolated git worktrees and provider-agnostic support.

MCP server that turns ChatGPT Web or any MCP client into a coding agent on your own repositories, enabling file editing, command execution, testing, and git workflow without an OpenAI API key.

An MCP server that enforces hard quality gates on AI coding agents, including forced web search, planning, and empirical test execution, to prevent common failure modes and ensure production-grade code.

An MCP server for local Cocos Creator 3.8 automation, enabling coding agents to create projects, edit scenes, and build WeChat Mini Games directly on a developer machine.

An MCP server for managing, editing, and running Stata .do files, integrating with Claude Desktop to enable natural language control of Stata analysis on macOS.

Self-hosted coding agent that runs Claude Code or Codex in your own infrastructure and works from Slack. Exposes codebase investigation, file edits, branch and PR creation, allowlisted command execution, CI status and an audit log as MCP tools, keeping every credential behind the tool layer instead of handing it to the model.