Factifai MCP Server

An MCP server for Factifai, enabling integration with any MCP-compatible AI tool to create and retrieve test results asynchronously.

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Factifai MCP Server

Hai Build Amazon Q VS Code Cursor Windsurf Zed

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.

Factifai MCP Server

Requirements

Node.js >= 16.0.0
Hai Build, Cursor, Windsurf, Claude Desktop or any MCP Client

Installation

# Latest version
npx --yes @presidio-dev/factifai-mcp-server@latest

# Specific version
npx --yes @presidio-dev/[email protected]

We recommend npx to install the server, but you can use any node package manager of your preference such as yarn, pnpm, bun, etc.

Installation Note

⚠️ 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.

Pre-Installation Tip

⚠️ 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

This 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

Configuration

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/[email protected]"],
		"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": []
	}
}

Environment Variables

Variable Name	Description
`MODEL_PROVIDER`	The model provider to use. (bedrock or openai)
`OPENAI_API_KEY`	The API key for the OpenAI model provider
`AWS_ACCESS_KEY_ID`	The AWS access key ID for the Bedrock model provider
`AWS_SECRET_ACCESS_KEY`	The AWS secret access key for the Bedrock model provider
`AWS_DEFAULT_REGION`	The AWS default region for the Bedrock model provider

Model Provider Configuration Examples

Bedrock Configuration Example

{
	"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": []
	}
}

OpenAI Configuration Example

{
	"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

See the setup instructions for each

Install in Hai Build

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>"
			}
		}
	}
}

Install in Amazon Q Developer

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>"
			}
		}
	}
}

Install in VS Code (Copilot)

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>"
      }
    }
  }
}

Install in Cursor

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>"
			}
		}
	}
}

Install in Windsurf

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>"
			}
		}
	}
}

Install in Zed

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>"
				}
			}
		}
	}
}

Available Tools

Tool Name	Description
`testWithFactifai`	Start a test with Factifai
`getFactifaiSessionResult`	Get test result
`listFactifaiSessions`	List tests

Contributing

We welcome contributions to the Factifai MCP Server! Please see our Contributing Guide for more information on how to get started.

Security

For information about our security policy and how to report security vulnerabilities, please see our Security Policy.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Factifai MCP Server

Factifai MCP Server

Table of Contents

Requirements

Installation

Installation Note

Pre-Installation Tip

Configuration

Environment Variables

Model Provider Configuration Examples

Bedrock Configuration Example

OpenAI Configuration Example

Factifai MCP integration with popular IDE and extension

Available Tools

Contributing

Security

License

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