"How to log in to Discord as a user" matching MCP servers:

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

Connects ChatGPT to Roblox Studio's built-in MCP, enabling natural-language reading and writing of game data, including executing Luau code and modifying scripts and instances.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Persistent memory and pattern learning for AI coding assistants via MCP.

Converts Figma designs to React Native components, allowing users to extract components from Figma designs and generate corresponding React Native components with proper typing and styling.

A Model Context Protocol (MCP) server that converts LaTeX mathematical expressions to MathML format.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Integrates ElevenLabs Text-to-Speech capabilities with Cursor through the Model Context Protocol, allowing users to convert text to speech with selectable voices within the Cursor editor.

Exposes the Grok Build CLI as MCP tools for Claude Code and other clients, enabling headless AI agents to perform tasks like code reviews, web research, and file operations with configurable permission levels.

Enables AI assistants to write and execute C# code directly inside Autodesk Civil 3D, providing full API access through code generation and execution.

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 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 MCP clients such as Claude Desktop or Cursor to inspect a coffee-shop sales database schema, validate SQL, and run guarded read-only queries. It supports natural-language questions by exposing tools for schema description, SQL checking, and safe query execution.

Exposes Dify applications (both Chatflow and Workflow) as MCP (Model Context Protocol) servers, allowing Claude and other MCP clients to directly interact with Dify apps through a standardized protocol.

A Model Context Protocol server that enables AI assistants to interact directly with Warden-managed Magento 2 development environments, automating common tasks like project initialization, environment management, database operations, and Magento CLI commands.

Enables Claude Code, Cursor, and other AI tools to call OpenAI Codex for task execution, with safe and writable modes.

Bridges Claude Desktop with Claude Code CLI to delegate complex coding tasks like creating React apps, building APIs, and debugging scripts while maintaining interaction through the Desktop interface.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Gives AI coding agents typed, mode-gated access to a real WordPress site — content, media, users and settings via core REST, plus PHP execution, SQL queries, filesystem operations and Elementor page read/build/patch/snapshot via a companion bridge plugin. Destructive actions and code execution are guarded by feature flags, explicit confirm literals, dry runs, redaction and audit logging.

Wraps Claude Code as a server using the Model Context Protocol (MCP), allowing teams to interact with Claude Code through a Slack bot interface.

A basic example MCP server deployed as a Netlify serverless function using Express. Demonstrates how to run Model Context Protocol servers in a serverless environment with proper routing configuration.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

Allows LLMs to execute Wolfram Language code in a secure, session-based environment by providing an interface to interact with a Wolfram Mathematica kernel.

An MCP bridge that lets Codex supervise Reasonix as an implementation worker in an isolated Git worktree, ensuring safe delegation with scoped writes and sandboxed execution.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.