testing-mcp
Let LLMs author your integration tests—E2E-style.
Install / Use
claude mcp add mcpland -- npx -y github:mcpland/testing-mcpIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Skill content
View source on GitHubTesting MCP
Write complex integration tests with AI - AI assistants see your live page structure, execute code, and iterate until tests work
Table of Contents
- Quick Start
- Why Testing MCP
- What Testing MCP Does
- Installation
- Configure MCP Server
- Connect From Tests
- MCP Tools
- Context and Available APIs
- Multi-Client Architecture
- CLI Commands
- Environment Variables
- FAQ
- How It Works
Quick Start
Step 1: Install
npm install -D testing-mcp
Step 2: Configure Model Context Protocol (MCP) server (e.g., in Claude Desktop config):
{
"testing-mcp": {
"command": "npx",
"args": ["-y", "testing-mcp@latest"]
}
}
Step 3: Connect from your test:
import { render, screen, fireEvent } from "@testing-library/react";
import { connect } from "testing-mcp";
it("your test", async () => {
render(<YourComponent />);
await connect({
context: { screen, fireEvent },
});
}, 600000); // 10 minute timeout for AI interaction
Step 4: Run with MCP enabled:
Prompt:
Please run the persistent test in the `examples/react-jest` directory:
`TESTING_MCP=true RTL_SKIP_AUTO_CLEANUP=true npm test test/App.test.tsx`
Then, use the `testing-mcp` tool to write the test by following these steps:
1. Click the button displaying "count is 0".
2. Verify that the button text changes to "count is 1".
3. Write the test code to a file.
Now your AI assistant can see the page structure, execute code in the test, and help you write assertions.
Why Testing MCP
Traditional test writing is slow and frustrating:
- Write → Run → Read errors → Guess → Repeat - endless debugging cycles
- Add
console.logstatements manually - slow feedback loop - AI assistants can't see your test state - you must describe everything
- Must manually explain available APIs - AI generates invalid code
Testing MCP solves this by giving AI assistants live access to your test environment:
- AI sees actual page structure (DOM), console logs, and rendered output
- AI executes code directly in tests without editing files
- AI knows exactly which testing APIs are available (screen, fireEvent, etc.)
- You iterate faster with real-time feedback instead of blind guessing
What Testing MCP Does
🔍 Real-Time Test Inspection
View live page structure snapshots, console logs, and test metadata through MCP tools. No more adding temporary console.log statements or running tests repeatedly.
🎯 Remote Code Execution
Execute JavaScript/TypeScript directly in your running test environment. Test interactions, check page state, or run assertions without modifying test files.
🧠 Smart Context Awareness
Automatically collects and exposes available testing APIs (like screen, fireEvent, waitFor) with type information and descriptions. AI assistants know exactly what's available and generate valid code on the first try.
await connect({
context: { screen, fireEvent, waitFor },
contextDescriptions: {
screen: "React Testing Library screen with query methods",
fireEvent: "Function to trigger DOM events",
},
});
🔄 Session Management
Reliable WebSocket connections with session tracking, reconnection support, and automatic cleanup. Multiple tests can connect simultaneously.
🚫 Zero CI Overhead
Automatically disabled in continuous integration (CI) environments. The connect() call becomes a no-op when TESTING_MCP is not set(particularly utilised hooks), so your tests run normally in production.
🤖 AI-First Design
Built specifically for AI assistants and the Model Context Protocol. Provides structured metadata, clear tool descriptions, and predictable responses optimized for AI understanding.
🔀 Multi-Client Support
Run multiple MCP clients simultaneously (Claude Desktop, Cursor, VS Code, etc.) without port conflicts. The daemon architecture automatically manages connections and port allocation.
Installation
Install dependencies and build the project before launching the MCP server or consuming the client helper.
npm install -D testing-mcp
# or
yarn add -D testing-mcp
# or
pnpm add -D testing-mcp
Node 18+ is required because the project uses ES modules and the WebSocket API.
Configure MCP Server
Add the MCP server to your AI assistant's configuration (e.g., Claude Desktop, VSCode, etc.):
{
"testing-mcp": {
"command": "npx",
"args": ["-y", "testing-mcp@latest"]
}
}
The server automatically discovers and connects to the bridge daemon, which manages WebSocket connections on dynamically assigned ports.
Connect From Tests
Import the client helper in your Jest or Vitest suites hook to expose the page state to the MCP server.
Example Jest setup file(setupFilesAfterEnv)
// jest.setup.ts
import { screen, fireEvent } from "@testing-library/react";
import userEvent from "@testing-library/user-event";
import { connect } from "testing-mcp";
const timeout = 10 * 60 * 1000;
if (process.env.TESTING_MCP) {
jest.setTimeout(timeout);
}
afterEach(async () => {
if (!process.env.TESTING_MCP) return;
const state = expect.getState();
await connect({
filePath: state.testPath,
context: {
userEvent,
screen,
fireEvent,
},
});
}, timeout);
It also supports usage in test files:
// example.test.tsx
import { render, screen, fireEvent, waitFor } from "@testing-library/react";
import userEvent from "@testing-library/user-event";
import { connect } from "testing-mcp";
it(
"logs the dashboard state",
async () => {
render(<Dashboard />);
await connect({
filePath: import.meta.url,
context: {
screen,
fireEvent,
userEvent,
waitFor,
},
// Optional: provide descriptions to help LLMs understand the APIs
contextDescriptions: {
screen: "React Testing Library screen with query methods",
fireEvent: "Synchronous event triggering function",
userEvent: "User interaction simulation library",
waitFor: "Async utility for waiting on conditions",
},
});
},
1000 * 60 * 10
);
Set TESTING_MCP=true locally to enable the bridge. The helper no-ops when the variable is missing or the tests run in continuous integration.
If the DOM has been automatically cleared after the
afterEachhook executes, please setRTL_SKIP_AUTO_CLEANUP=true.
MCP Tools
Once connected, your AI assistant can use these tools:
| Tool | Purpose | When to Use |
| ------------------------ | ------------------------------------------------------ | --------------------------------------------------- |
| get_current_test_state | Fetch current page structure, console logs, and APIs | Inspect what's rendered and what APIs are available |
| execute_test_step | Run JavaScript/TypeScript code in the test environment | Trigger interactions, check state, run assertions |
| finalize_test | Remove connect() call and clean up test file | After test is complete and working |
| list_active_tests | Show all connected tests with timestamps | See which tests are available |
| get_generated_code | Extract code blocks inserted by the helper | Audit what code was added |
get_current_test_state
Returns the current test state including:
- Page structure snapshot: Current rendered HTML (DOM)
- Console logs: Captured console output
- Test metadata: Test file path, test name, session ID
- Available context: List of all APIs/variables available in
execute_test_step, including their types, signatures, and descriptions
Response includes availableContext field:
{
"availableContext": [
{
"name": "screen",
"type": "object",
"description": "React Testing Library screen object"
},
{
"name": "fireEvent",
"type": "function",
"signature": "(element, event) => ...",
"description": "Function to trigger DOM events"
}
]
}
execute_test_step
Executes JavaScript/TypeScript code in the connected test client. The code can use any APIs listed in the availableContext field from get_current_test_state.
Best Practice: Always call get_current_test_state first to check which APIs are available before using execute_test_step.
Context and Available APIs
Inject testing utilities so AI knows what's available:
The connect() function accepts a context object that exposes APIs to the test execution environment. This allows AI assistants to know exactly what APIs are available when generating code.
Basic Usage
await connect({
context: {
screen, // React Testing Library queries
fireEvent, // DOM event triggering
userEvent, // User interaction simulation
waitFor, // Async waiting utility
},
});
Adding Descriptions (Recommended)
Provide descriptions for each context key to help AI understand what's available:
await connect({
context: {
screen,
fireEvent,
waitFor,
customHelper: async (text: string) => {
const button = screen.getByText(text);
fireEvent.click(button);
await waitFor(() => {});
},
},
contextDescriptions: {
screen: "Query methods like getByText, findByRole, etc.",
fireEvent: "Trigger DOM events: click, change, etc.",
waitFor: "Wait for assertions: waitFor(() => expect(...).toBe(...))",
customHelper: "async (text: string) => void - Clicks button by text",
},
});
How it works: The client collects metadata (name, type, function signature) for each context key. When AI calls get_current_test_state, it receives the full list of available APIs with their metadata, enabling accurate code generation.
Multi-Client Architecture
Testing MCP v0.4.0 introduces a Daemon + Adapter architecture that allows multiple MCP clients to work simultaneously without port conflicts.
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ MCP Client A (Claude Desktop) │
│ ↓ │
│ testing-mcp serve (Adapter A) ──┐ │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ MCP Client B (Cursor) │
│ ↓ │
│ testing-mcp serve (Adapter B) ──┼── RPC ──→ Bridge Daemon │
└─────────────────────────────────────────────────────────────────┘
│ (Single Instance)
┌─────────────────────────────────────────────────────────────────┘
│ MCP Client C (VS Code) │
│ ↓ │
│ testing-mcp serve (Adapter C) ──┘ │ │
└─────────────────────────────────────────────────────────────────┘
↓
Truncated for display — read the full file on GitHub.
Related Skills
caveman
107.1k🪨 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman.
claude-mem
94.4kPersistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
Agent-Reach
84.2kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Understand-Anything
83.5kGraphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
