"Creating custom experimental images in Docker" matching MCP servers:

Enables natural-language management of Docker containers, images, volumes, and networks, with autonomous diagnosis and remediation of container failures through a multi-agent system.

Enables AI assistants to interact with Docker containers through safe, permission-controlled access to inspect, manage, and diagnose containers, images, and compose services with built-in timeouts and AI-powered analysis.

Enables natural language management of Docker containers, images, networks, and volumes with support for both local and remote Docker engines. Features automated container composition, debugging capabilities, and persistent data management through an intuitive conversational interface.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

An MCP server for Microsoft's Cognitive Services Custom Vision Training API that enables AI agents to create, train, and manage custom image classification and object detection models through natural language interactions.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

An MCP server for analyzing images using OpenRouter vision models, offering capabilities like automatic image resizing, model configuration, and handling custom queries about images.

Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables isolated shell command execution, file and process management, native image inspection, downstream tool-server proxying, persistent thread goals, and token-usage accounting through a live dashboard.

Enables real-time full-duplex voice agents to classify user interruptions, backchannels, and acoustic echo, and to arbitrate turn-taking with context rollback via MCP.

Enables real-time voice agents to arbitrate full-duplex speech by classifying user interruptions, backchannels, and acoustic echo while supporting context rollback.

Enables agents to query the TypeSafe Jev decision model for typed answers—yes/no, choice, and score questions—with calibrated probabilities, batched in a single request under a local context-budget guard.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

Utilizes the Google Gemini API to generate context-specific categories and diverse options for creative brainstorming. It features a random sampling function to help users bypass predictable AI patterns and discover unexpected, innovative ideas.

Enables autonomous agents and MCP-compatible clients to enforce policy-approved actions using risk tiers, reversible checkpoints, and token approval bounds.

Enables autonomous agents and multi-agent frameworks to route actions through deterministic human approval gates before execution. Enforces risk tiers, reversible checkpoints, and token approval bounds so that risky or costly operations can be reviewed, bounded, and rolled back without LLM round-trips.

A task-level STDIO MCP server that lets Codex or any other MCP client hand off scoped coding jobs to an asynchronous worker agent which reads the code, edits files, and runs tests, while the client keeps ownership of planning and acceptance. Exposes submit, wait, query, follow-up, and cancel tools so multiple clients can queue and monitor tasks against a chosen project root.

Exposes a fleet's agent services to AI agents: browsing the public agent directory, fetching AgentCards, managing deployer allowlists, pausing or resuming agents, and running A2A conversation turns under a dual-credential model (management tokens vs. caller keys).

Authentik MCP provides seamless integration with Authentik's API, supporting both full-featured and diagnostic modes. These enable secure, automated user, group, and system management through MCP-compatible tools.