"How to connect to a React Native development build" matching MCP servers:

Provides comprehensive tools for React Native development, automating project initialization, version management, upgrades, Expo integration, and development workflows through AI assistance.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A Model Context Protocol server that converts Markdown content to HTML format.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Enables MCP clients such as Claude Desktop to hand tasks to a running DeepSeek Harness window, where each task becomes a real session visible in the sidebar with a reusable session id. If the in-process plugin is unavailable, it falls back to running the task headless so the tool still returns a result.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

MCP server for qualifying and responding to inbound leads in seconds using a multi-agent AI pipeline.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

Enables two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Enables Claude Code agents to perform structured decision-making through TypeSafe Jev, supporting yes/no checks, route selection, scoring, and batched multi-question judgments with confidence values.

Enables orchestrator agents to route coding tasks to native CLI worker agents via MCP, providing tools for agent discovery, task submission, waiting, inspection, and cancellation.

An MCP server that lets Claude Code delegate durable background tasks, reasoning profiles, thread resumption, and native image generation to your local Codex CLI.

Enables AI agents to perceive, navigate, interact with, and build inside Minecraft through typed, bounded operations, including deterministic blueprints, verified builds, repair, and creative-mode flight or teleport. Runs as a provider-neutral MCP server or an interactive dev shell, exposing movement, voxel manipulation, inventory, chat, and diagnostics tools.

Enables AI agents to call tools through a security gateway that intercepts requests, enforces deterministic policy and risk checks, blocks or escalates high-risk actions for human approval, and keeps tool credentials and audit logs protected.

Bridges a planning and review client to DeepSeek Harness Desktop over local stdio MCP, letting users submit staged tasks with explicit goals, scope, and acceptance criteria for execution inside native DSH sessions, models, and approval boundaries. Exposes task submission, waiting, evidence retrieval, review, continuation, cancellation, and recovery tools so results are judged against traceable evidence from the current run.

Enables AI agents to connect to Revit 2024 via MCP, inspect documents and selections, query and modify elements, and validate, preview, or commit plans with audit logging and rollback.

This server enables MCP clients to control a real Minecraft Java player client with native first-person gameplay, structured spatial perception, bounded continuous actions, and optional livestream widgets.

Enables macOS computer-use automation through public Apple APIs, supporting app and window discovery, clicking, typing, scrolling, and field setting via accessibility controls.

MCP server that provides a single run_grok tool to execute Grok build agent tasks in a specified working directory, automatically reusing sessions and returning results with logs.

Enables AI coding agents to compile Delphi projects programmatically by parsing .dproj files, executing the Delphi compiler, and returning structured error results with multi-language support and automatic configuration generation from IDE build logs.

Enables AI agents to profile React applications, detect performance bottlenecks, and automatically apply fixes like memoization and code hoisting.

Enables AI agents to resolve social handles to wallet addresses, check reputation scores, and send tokens directly from any MCP-compatible client.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.