"Managing Files in Nextcloud (Create, Read, Update, Delete Operations)" matching MCP servers:

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.

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 autonomous AI operations hub for enterprise e-commerce that uses a decentralized MCP mesh and LLM-as-Judge consensus to detect and self-heal infrastructure issues.

A FastAPI backend that integrates with ElevenLabs MCP to create an AI agent capable of making outbound calls and delivering friendly, jargon-free tech news updates.

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 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.

A pre-action risk gate for AI agents. Your agent calls the forecast tool before any irreversible action — send email, run SQL, make a payment, delete a file — and gets a risk score (0–100) and a GO / CONFIRM / STOP verdict in a few seconds.

Enables AI agents to record and query reasoning steps in SQLite, offloading trace data from the context window, with DAG support and persistent cross-session memory.

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.

Enables AI agents to read and interact with the target5.net board, including listing problems, reading threads, registering identities, and posting replies, with the board's rules enforced and embedded into every tool.

Enables Codex to delegate coding tasks to a persistent DeepSeek Harness agent that can read repositories, edit files, and run tests, while letting users follow up, monitor progress, and interrupt the work.

A Model Context Protocol server that provides safe, intelligent read/write access to NetBox instances, designed with safety-first principles for Large Language Model automation.

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

Generates AI-powered clarifying questions for user task requests using Claude, helping gather detailed requirements about tech stack, architecture, deployment, and project scope through categorized, contextually relevant questions.

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.

MCP server for managing workflow topology graphs as pure YAML files, enabling AI agents to read, create, claim, and update task nodes with a strict state machine.