Factifai MCP Server
About
An MCP server for Factifai, enabling integration with any MCP-compatible AI tool to create and retrieve test results asynchronously.
Details
- Author
- presidio-oss
- Categories
- Developer Tools, API, Automation
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Setup
Install Factifai MCP Server in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/presidio-oss/factifai-mcp-server
Follow the installation instructions in the repository README, then restart your MCP client.
A Model Context Protocol (MCP) server forFactifaiintegration 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.
- Table of Contents
- Requirements
- Installation
- Installation Note
- Pre-Installation Tip
- Environment Variables
- Model Provider Configuration Examples
- Bedrock Configuration Example
- OpenAI Configuration Example
- 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@1.2.3
We recommendnpxto install the server, but you can use any node package manager of your preference such asyarn,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.
- 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@latest
- 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
{ "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": [] } }
{ "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": [] } }
{ "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": [] } }
Factifai MCP integration with popular IDE and extension
Add the following to yourhai_mcp_settings.jsonfile. To open this file from Hai Build, click the "MCP Servers" icon, select the "Installed" tab, and then click "Configure MCP Servers".
See theHai Build MCP documentationfor 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. SeeMCP configuration for Q Developer in the IDEfor more details.
The configuration file can be stored globally at~/.aws/amazonq/mcp.jsonto 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 formcp.enabled, and checking the box.
Then, add the following configuration to your user or workspacesettings.jsonfile. See theVS Code MCP documentationfor 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 yourmcp.jsonfile. This file can be located globally at~/.cursor/mcp.jsonor within a specific project at.cursor/mcp.json. See theCursor MCP documentationfor 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.jsonfile. See theWindsurf MCP documentationfor 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 yoursettings.jsonfile (accessible via thezed: settingsaction) or by using the Agent Panel's configuration UI (agent: open configuration). See theZed MCP documentationfor more information.
Add the following to yoursettings.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>" } } } } }
We welcome contributions to the Factifai MCP Server! Please see ourContributing Guidefor more information on how to get started.
For information about our security policy and how to report security vulnerabilities, please see ourSecurity Policy.
This project is licensed under the MIT License - see theLICENSEfile for details.
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