hangwin/mcp-chrome
mcp-chrome
Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, content analysis, and semantic search.
Usage guide
mcp-chrome is an open-source project around mcp, search with 12,001 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating mcp-chrome for TypeScript AI workflows.
- Comparing a GitHub project with 12,001 stars and current repository activity.
Pros
- mcp-chrome has visible GitHub traction with 12,001 stars.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
mcp-chrome should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
mcp-chrome architecture preview
mcp-chrome's main path starts at the entry surface, runs through Coding agent runtime, combines Claude, Vector index, GitHub / MCP tools / Browser automation, and returns Grounded answers / search results.
Entry
CLI / terminal entry
mcp-chrome is primarily entered through a developer command or terminal workflow.
npm install -g mcp-chrome-bridge
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
Claude
Model calls are likely routed through Claude based on README and topic signals.
Claude
Context
Vector index
Context comes from Vector index, which constrains what the model or runtime can use.
Vector index
Tools
GitHub / MCP tools / Browser automation
Tool adapters let the runtime act outside the model through GitHub / MCP tools / Browser automation.
GitHub, MCP tools, Browser automation
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
mcp-chrome uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/hangwin/mcp-chrome.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npm install -g mcp-chrome-bridgeAdoption guidance and sources
Practical use cases
Chrome MCP Server is a Chrome extension-based Model Context Protocol (
This is one of the documented reasons to evaluate mcp-chrome before choosing a stack.
MCP project comparison
Compare mcp-chrome with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official mcp-chrome setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented mcp-chrome example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is mcp-chrome?
mcp-chrome is an open-source mcp project. Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, content analysis, and semantic search.
How do I install mcp-chrome?
Start with the official README. The first detected setup step is: git clone https://github.com/hangwin/mcp-chrome.git.
Is mcp-chrome beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can mcp-chrome be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does mcp-chrome need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt mcp-chrome?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.