Enables test creation and execution through Factifai using OpenAI as the model provider for AI-powered testing capabilities.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Factifai MCP Server create a test for user login functionality"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for Factifai integration with any MCP-compatible AI tool. This server is designed to be tool-agnostic, meaning it can be used with any tool that supports the MCP protocol. This server currently exposes tools to create tests asynchronously and get the result of the test.
Related MCP server: sentinel-mcp
Node.js >= 16.0.0
Hai Build, Cursor, Windsurf, Claude Desktop or any MCP Client
# Latest version
npx --yes @presidio-dev/factifai-mcp-server@latest
# Specific version
npx --yes @presidio-dev/factifai-mcp-server@1.2.3We recommend npx to install the server, but you can use any node package manager of your preference such as yarn, pnpm, bun, etc.
⚠️ Important: The first time you install Factifai MCP Server, it will automatically download and install browser dependencies using Playwright. This process may take several minutes depending on your internet connection and system specifications.
The installation includes:
Downloading browser binaries (Chromium, Firefox, WebKit)
Installing browser dependencies
Setting up the necessary environment
This happens only once, and subsequent runs will be much faster as the browsers are already installed.
⚠️ Recommended for First-Time Installation: Many MCP clients have strict timeout limits for server startup. The browser installation process during first-time setup may exceed these timeouts, causing the installation to fail or appear non-responsive.
To avoid timeout issues, we strongly recommend pre-installing Playwright browsers manually:
# Step 1: Install Playwright browsers manually before installing the MCP server
npx playwright install --with-deps
# Step 2: Then install the MCP server (will be much faster and avoid timeouts)
npx --yes @presidio-dev/factifai-mcp-server@latestThis pre-installation step:
Ensures browsers are downloaded without MCP client timeout constraints
Significantly speeds up the MCP server's first-time installation
Prevents installation failures due to timeout issues in your IDE or MCP client
with npx with latest version:
{
"factifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
},
"disabled": false,
"autoApprove": []
}
}with npx with specific version:
{
"factifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/factifai-mcp-server@1.2.3"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
},
"disabled": false,
"autoApprove": []
}
}Variable Name | Description |
| The model provider to use. (bedrock or openai) |
| The API key for the OpenAI model provider |
| The AWS access key ID for the Bedrock model provider |
| The AWS secret access key for the Bedrock model provider |
| The AWS default region for the Bedrock model provider |
{
"factifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
},
"disabled": false,
"autoApprove": []
}
}{
"factifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "openai",
"OPENAI_API_KEY": "<your-openai-api-key>"
},
"disabled": false,
"autoApprove": []
}
}See the setup instructions for each
Add the following to your hai_mcp_settings.json file. To open this file from Hai Build, click the "MCP Servers" icon, select the "Installed" tab, and then click "Configure MCP Servers".
See the Hai Build MCP documentation for more info.
{
"mcpServers": {
"factifai": {
"command": "npx",
"args": ["-y", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}Add the following to your Amazon Q Developer configuration file. See MCP configuration for Q Developer in the IDE for more details.
The configuration file can be stored globally at ~/.aws/amazonq/mcp.json to be available across all your projects, or locally within your project at .amazonq/mcp.json.
{
"mcpServers": {
"factifai": {
"command": "npx",
"args": ["-y", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}
First, enable MCP support in VS Code by opening Settings (Ctrl+,), searching for mcp.enabled, and checking the box.
Then, add the following configuration to your user or workspace settings.json file. See the VS Code MCP documentation for more info.
"mcp": {
"servers": {
"factifai": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}The easiest way to install is with the one-click installation button below.
Alternatively, you can manually configure the server by adding the following to your mcp.json file. This file can be located globally at ~/.cursor/mcp.json or within a specific project at .cursor/mcp.json. See the Cursor MCP documentation for more information.
{
"mcpServers": {
"factifai": {
"command": "npx",
"args": ["--yes", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}Add the following to your ~/.codeium/windsurf/mcp_config.json file. See the Windsurf MCP documentation for more information.
{
"mcpServers": {
"factifai": {
"command": "npx",
"args": ["-y", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}You can add the Factifai MCP server in Zed by editing your settings.json file (accessible via the zed: settings action) or by using the Agent Panel's configuration UI (agent: open configuration). See the Zed MCP documentation for more information.
Add the following to your settings.json:
{
"context_servers": {
"factifai": {
"command": {
"path": "npx",
"args": ["-y", "@presidio-dev/factifai-mcp-server@latest"],
"env": {
"MODEL_PROVIDER": "bedrock|openai",
"OPENAI_API_KEY": "<your-openai-api-key>",
"AWS_ACCESS_KEY_ID": "<your-aws-access-key-id>",
"AWS_SECRET_ACCESS_KEY": "<your-aws-secret-access-key>",
"AWS_DEFAULT_REGION": "<your-aws-region>"
}
}
}
}
}Tool Name | Description |
| Start a test with Factifai |
| Get test result |
| List tests |
We welcome contributions to the Factifai MCP Server! Please see our Contributing Guide for more information on how to get started.
For information about our security policy and how to report security vulnerabilities, please see our Security Policy.
This project is licensed under the MIT License - see the LICENSE file for details.
This server cannot be deployed
Discover and call AI agents via MCP. Supports A2A agents and platform agents with async tasks.
Run, debug, and triage tests from your IDE using natural language, no dashboard switching, no manual data transfers. The TestMu AI (formerly LambdaTest) MCP Server is a single remote server exposing four tool suites: HyperExecute — analyze your project, generate YAML configs and test runner commands, then monitor jobs and sessions. Automation — pull a TestID's details plus command, network, and console logs into one chat for instant root-cause analysis. Includes mobile app upload. SmartUI — explain pixel, layout, DOM, and perceptual changes in a visual regression run, with context-aware React/HTML/CSS fixes. Accessibility — audit any public URL or a local React app against WCAG and get ready-to-apply remediation steps. Connects over https://mcp.lambdatest.com/mcp using OAuth 2.1 — no API keys in your config. One-click install in Cursor; works with Claude, GitHub Copilot, Cline, and any MCP client. Tests execute on the TestMu AI cloud: 3,000+ browsers and 10,000+ real devices.
MCP server for AI access to SmartBear tools, including BugSnag, Reflect, Swagger, PactFlow, QTM4J.
AI-callable tools for API mocking, testing, monitoring, security, and automation.
