by amandeep-sg
MCP tools build using selenium to automate web testing or scraping
★ 2
GitHub stars
Bridges AI assistants with Selenium WebDriver to enable web automation, testing, and scraping through comprehensive browser control tools.
selenium_mcp is a community-built MCP server published by amandeep-sg that provides AI assistants with tools and capabilities via the Model Context Protocol. MCP tools build using selenium to automate web testing or scraping It is categorized under productivity. This server exposes 20 tools that AI clients can invoke during conversations and coding sessions.
You can install selenium_mcp in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
selenium_mcp is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Useful MCP listing: selenium_mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend selenium_mcp for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Useful MCP listing: selenium_mcp is the kind of server we cite when onboarding engineers to host + tool permissions.
selenium_mcp is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: selenium_mcp surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated selenium_mcp against two servers with overlapping tools; this profile had the clearer scope statement.
We wired selenium_mcp into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
selenium_mcp has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
selenium_mcp is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
According to our notes, selenium_mcp benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
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This server is implemented in python to bridge the gap between the AI Assistant or (custom MCP clients) and Selenium Webdrivers. It exposes selenium webdriver functionalities as MCP tools allowing AI assistanct/MCP clients to user them to perform task for web automation, web testing or web scraping.
In this version, we have done some structural changes like seperating functions into save and get. Now save is just focused on saving files on the disk. And get is where LLM wants to get the data from the browser.
Following are the list of enhancements:
The tools leverages following technologies to support
Following are the list of features that will be added in the future:
Prompt: "Open https://rfpnotification.com and join the waiting list by entering the email address: [[email protected]]"
| Before | After |
|---|---|
 |  |
After running the script, the browser took the screenshot to check if the email was entered successfully.
Clone the repository
git clone {url}
Create virtual environment
python3 -m venv venv
source venv/bin/activate
Install dependencies
pip install -r requirements.txt
Run the server
python server.py
The package comes with a lightweight MCP client using Google GenAI SDK to test the server. It is implemented in server.py file. To use it, you need to have a Google GenAI API key. Set it in the .env file as GEMINI_API_KEY={your_api_key}.
Run the client
python server.py
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.