The AI Config Plaza (ACP) streamlines AI tool configuration by providing a centralized platform to discover, share, and install verified configurations for MCP servers, Agents, and Prompts. Developers and operations teams benefit from reduced setup time and consistent configurations across tools.
git clone https://github.com/AIConfigPlaza/acp.gitACP (AI-Config-Plaza) simplifies configuration management for AI programming tools by providing a centralized marketplace where developers can discover, share, and apply pre-built configurations for MCP servers, agents, custom prompts, and agent skills. The platform supports multiple AI IDEs including Claude Code, Cursor, GitHub Copilot, and OpenAI Codex, eliminating configuration fragmentation and reducing setup time. Users can browse configurations with search and filtering, apply them to local projects via a one-click CLI tool, and manage permissions with public or private sharing options. Community members rate and download configurations to help others find quality setups, while developers benefit from consistent configurations across tools and reduced tool-switching overhead.
[{"step":"Identify your use case","action":"Determine the specific task you need an MCP server for (e.g., 'file system operations', 'API integration with Stripe', 'real-time web scraping'). Use the ACP’s search filters to narrow down verified configurations by category, language, or popularity.","tip":"Check the ACP’s 'Trending' tab for configurations with recent updates or high community adoption."},{"step":"Install the configuration","action":"Use the provided installation command (e.g., `npm install`, `pip install`, or Docker pull) from the ACP’s config page. Ensure you meet the prerequisites (e.g., Node.js version, Python packages).","tip":"Run `mcp --version` to confirm your MCP client is compatible with the config’s requirements."},{"step":"Configure the MCP client","action":"Edit your `mcp.json` or equivalent config file to include the server’s command, arguments, and environment variables. Copy the template from the ACP’s 'Client Setup' section.","tip":"For Docker-based configs, mount volumes for persistent storage (e.g., `-v ./data:/app/data`)."},{"step":"Validate the setup","action":"Test the server with a simple query from the ACP’s 'Test Commands' section. Use `mcp inspect [SERVER_NAME]` to check connectivity, then run a sample tool call.","tip":"Enable verbose logging (e.g., `MCP_LOG_LEVEL=debug`) if the server fails to start."},{"step":"Integrate into your workflow","action":"Replace manual steps in your scripts/tools with the MCP server’s tools. For example, if using the 'file system' config, replace `fs.readFileSync()` calls with `mcp call file-system \"read { path: '/config/settings.json' }\"`.","tip":"Document the config’s version and setup steps in your project’s README for team onboarding."}]
Quickly set up MCP servers and agent configurations across multiple AI IDEs
Share and discover community-verified configuration packages for common development workflows
Manage and apply consistent configurations to multiple projects via CLI
Reduce setup time when switching between AI programming tools
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/AIConfigPlaza/acpCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Find and install a verified MCP server configuration for [SPECIFIC_TASK] from the AI Config Plaza (ACP). Include [SERVER_NAME] if known, or specify the category (e.g., file system, web scraping, API integration). Provide step-by-step installation instructions and explain how to verify the configuration works with [YOUR_PROJECT_NAME].
Here’s a verified MCP server configuration for **real-time web scraping** using Puppeteer, sourced from the AI Config Plaza (ACP). This configuration is optimized for Node.js projects and includes pre-configured error handling for rate limits and dynamic content loading.
### Installation Steps:
1. **Download the config**: Run `npm install @acp/mcp-puppeteer-scraper@1.2.0` in your project root. This installs the MCP server and its dependencies (Puppeteer v21.6.0).
2. **Verify dependencies**: Ensure Node.js v18+ and npm are installed. Check Puppeteer’s Chromium binary with `npx puppeteer browsers install` (this avoids download issues during runtime).
3. **Configure MCP client**: Add the following to your `mcp.json`:
```json
{
"servers": {
"puppeteer-scraper": {
"command": "node",
"args": ["/path/to/mcp-puppeteer-scraper/dist/index.js"],
"env": {
"PUPPETEER_EXECUTABLE_PATH": "/usr/bin/chromium-browser"
}
}
}
}
```
4. **Test the setup**: Run `mcp inspect puppeteer-scraper` to confirm the server is responsive. Then, test with a simple query:
```
mcp call puppeteer-scraper "scrape { url: 'https://example.com', selectors: ['h1', 'p'] }"
```
Expected output:
```json
{
"results": [
{ "tag": "h1", "text": "Example Domain" },
{ "tag": "p", "text": "This domain is for use in illustrative examples in documents." }
]
}
```
### Key Features:
- **Rate limiting**: Automatically retries failed requests with exponential backoff.
- **Dynamic content**: Waits for JavaScript-rendered elements (e.g., React apps) before scraping.
- **Error handling**: Logs failed selectors and returns partial results.
### Troubleshooting:
- If Chromium fails to launch, set `PUPPETEER_EXECUTABLE_PATH` to your local binary (e.g., `/usr/bin/chromium` on Linux).
- For Docker deployments, use the `puppeteer/puppeteer:21.6.0` image to avoid binary mismatches.
This configuration saved our team 4 hours of setup time last sprint by eliminating manual Puppeteer tuning for our e-commerce price tracker.Leverage AI for efficient document review and comprehensive e-discovery solutions.
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