react-native-dev-mcp
MCP server that gives AI coding agents hands, eyes and a mechanic's ear for React Native development.
README
react-native-dev-mcp
MCP server that gives AI coding agents hands, eyes and a mechanic's ear for React Native development.
Coding agents are good at writing React Native code and bad at everything around it: putting the app on a device, seeing what it logs, and understanding why the native build exploded into 4,000 lines of Gradle. This server closes that loop with three pillars:
| Pillar | What the agent gets |
|---|---|
| ๐คฒ Device control | One uniform interface over simctl + adb: list/boot devices, install/launch apps, deep links, screenshots, demo status bar. The agent never needs to know which platform it's driving. |
| ๐ Runtime bridge | Connects to Metro's inspector (CDP/Hermes, RN 0.76+ "Fusebox"). Streams console.* and uncaught errors into a cursor-based buffer, and runs evaluate_js inside the live app โ inspect Redux/Zustand state, trigger navigation, verify the fix actually worked. Zero app code changes. |
| ๐ง Build diagnostics | Runs xcodebuild/Gradle as background jobs and parses the log against a community-maintained signature database of known RN failures, returning {errorType, file, line, probableCause, suggestedFix} instead of a log dump. Also parses any existing log offline. |
Works with Expo and bare React Native CLI, RN 0.76+ (tested on 0.80+).
The server is environment-aware: on Windows/Linux, iOS tools are never even registered, and
every failure returns a structured remediation the agent can act on. Start with the
doctor tool.
Install
Requires Node 22+. The server runs on your machine over stdio.
Claude Code
claude mcp add rn-dev -- npx -y react-native-dev-mcp
Or per-project, in .mcp.json:
{
"mcpServers": {
"rn-dev": {
"command": "npx",
"args": ["-y", "react-native-dev-mcp"]
}
}
}
Codex CLI
In ~/.codex/config.toml:
[mcp_servers.rn-dev]
command = "npx"
args = ["-y", "react-native-dev-mcp"]
Cursor / Claude Desktop / others
Any MCP client that speaks stdio works โ point it at npx -y react-native-dev-mcp.
Useful flags: --project-root <path>, --metro-port <port>, --eager-metro, --verbose.
Try asking your agent
Once installed, talk to your agent in plain language โ it picks the right tools:
"Run my app on Android and figure out why it's broken at startup."
"Boot an iPhone simulator, build and install the app, then screenshot the login screen."
"The iOS build is failing. Diagnose it, apply the suggested fix and rebuild until it's green."
"Watch the console while I tap through checkout and summarize any errors you see."
"What's in the Redux store right now? Read the auth slice from the running app."
"Open the deep link myapp://profile/42 and verify it lands on the right screen."
The loop
A typical agent session against a real app:
doctor โ toolchains ok, Metro running, project: expo ยท RN 0.85
list_devices โ iPhone 16 Pro (booted)
run_build {platform: "ios"} โ job a1b2c3d4
get_build_status {jobId} โ โ failed ยท "Sandbox not in sync with Podfile.lock"
โ fix: run pod install (tool run_pod_install)
run_pod_install โ โ
run_build โ get_build_status โ โ
artifact: .../MyApp.app
install_app + launch_app โ app running
read_console {level:"error"} โ "TypeError: cannot read property 'id' of undefined"
evaluate_js {expression:
"globalThis.store.getState().auth"} โ { user: null, โฆ } โ found it
(agent edits code) โ reload_app โ read_console โ clean โ
take_screenshot โ ๐ธ verified visually
Tools (20)
Environment ยท doctor
Devices ยท list_devices boot_device shutdown_device install_app uninstall_app launch_app terminate_app open_url take_screenshot set_status_bar_demo
Runtime ยท list_runtime_targets read_console evaluate_js reload_app
Build ยท run_build get_build_status cancel_build run_pod_install parse_build_log
Full reference with schemas and error codes: documentation site.
Good to know
- One debugger at a time (Hermes limitation). If React Native DevTools is attached, runtime
tools return
DEBUGGER_OCCUPIEDwith instructions. Close the DevTools tab and retry. - Builds never block.
run_buildreturns a job id in <2s; poll withget_build_status(it long-polls up to 60s per call). Logs persist on disk and can be re-parsed any time withparse_build_log. - Token-frugal by design. Lists are collapsed and capped, console reads are cursor-based and
deduped, build results carry at most 10 diagnostics plus a
logPathescape hatch.
Contributing
The easiest high-impact contribution is a build error signature โ pure YAML plus a log
snippet, no TypeScript. See CONTRIBUTING.md. Development is spec-driven:
every tool's contract lives in specs/.
License
MIT ยฉ Luiz Esper
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.