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

Enables comprehensive Windows system management through Claude Desktop, including PowerShell/CMD execution, file operations, archive handling, Git tools, network testing, and system monitoring. Provides a complete toolkit for Windows automation and administration tasks.

Enables database inspection, backup/restore, analysis, and querying, plus media library and Windows Registry management via MCP tools.

Read-only MCP server for the GitHub REST API that enables agents to query repositories, issues, files, and users without any write access.

Enables AI agents to build and structurally validate Indian GST e-invoice JSON payloads (INV/CRN/DBN) and render IRP-returned signed QR strings as PNG images.

Search reusable technical experience from AI agents, inspect evidence and limitations, and discover public agent Trust Passports through seven anonymous read-only MCP tools. Apache-2.0 integrations, examples and documentation connect to the separately governed hosted Remnant service. Agent OAuth and Candy are separate surfaces.

Enables following a creator's bio link to detect their newsletter, products sold, and contact emails across supported platforms and link-in-bio pages.

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.

Navigator for BPC Agentic Procurement, providing tools for submitting RFQs, checking status, requesting terms, and escalating to a human.

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FableCut is a Premiere-style non-linear video editor that runs entirely in your browser — and exposes its whole timeline as one JSON document. Edit it by hand, from the UI, or let an AI agent (Claude Code, Claude Desktop, or anything that speaks MCP/REST) cut your video for you while you watch the timeline update live.

Read selected local folders from ChatGPT on macOS, with per-folder read-only or read-write access and hash-checked, recoverable edits. Requires local installation and a private ChatGPT app. The Glama container is a synthetic evaluation demo; it does not connect to files on your Mac.

Review QQ Mail subscription, renewal and billing notices locally on macOS through a read-only stdio MCP server. Strict TLS and PEEK reads; no sending or mailbox changes. Requires Python 3.13+ and session-only credentials entered through native macOS dialogs; no hosted mailbox or automatic monitoring.

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 agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

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

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

Real-time Amazon Sponsored Products (SP) ad placements, keyword tracking, and comprehensive review data for AI Agents. Enables LLMs to autonomously conduct competitor ad audits, consumer sentiment analysis (VOC), and product optimization.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.