Go Playground▌
by samber
Run and share Go code instantly with Go Playground. Features include code execution, vet checking, and public URLs for e
Enables remote execution of Go code using the official Go Playground API with tools for running code with optional vet checking, sharing snippets via public URLs, and combining execution with sharing for immediate testing and distribution.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Go developers testing code snippets
- / Sharing Go examples with colleagues
- / Code review and collaboration
- / Learning and experimenting with Go
capabilities
- / Execute Go code in sandbox environment
- / Generate shareable URLs for Go snippets
- / Read code from existing Go Playground URLs
- / Run code with optional vet checking
- / Combine execution and sharing in single operation
what it does
Execute Go code remotely using the official Go Playground API and share code snippets with public URLs.
about
Go Playground is a community-built MCP server published by samber that provides AI assistants with tools and capabilities via the Model Context Protocol. Run and share Go code instantly with Go Playground. Features include code execution, vet checking, and public URLs for e It is categorized under developer tools. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install Go Playground 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.
license
MIT
Go Playground is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Go Playground MCP Server
A Model Context Protocol (MCP) server that integrates with the Go Playground API to execute Go code and generate shareable URLs.
🧙 Features
- Run Go Code: Execute Go code in the Go Playground sandbox
- Share Code: Generate shareable URLs for Go code snippets
- Run and Share: Execute code and get both results and share URL in one operation
- Read from URL: Read Go code from existing Go Playground URLs
- Execute from URL: Execute Go code from existing Go Playground URLs
- MCP Integration: Full Model Context Protocol compliance
🏃♂️ Usage
The server can be used with any MCP-compatible client. The server provides five tools:
run_go_code- Execute Go code and return resultsshare_go_code- Share Go code and get a URLrun_and_share_go_code- Execute code and get both results and share URLread_go_playground_url- Read Go code from an existing Go Playground URLexecute_go_playground_url- Execute Go code from an existing Go Playground URL
Add this to your MCP client configuration:
{
"mcpServers": {
"go-playground": {
"command": "npx",
"args": ["-y", "go-playground-mcp"]
}
}
}
Examples
Reading code from a Go Playground URL
// Read code from https://go.dev/play/xyz123
const result = await mcpClient.callTool("read_go_playground_url", {
url: "https://go.dev/play/xyz123"
});
Executing code from a Go Playground URL
// Execute code from https://go.dev/play/xyz123
const result = await mcpClient.callTool("execute_go_playground_url", {
url: "https://go.dev/play/xyz123",
withVet: true
});
URL Formats Supported
The new URL-based tools support these Go Playground URL formats:
https://go.dev/play/<snippet-id>https://go.dev/play/p/<snippet-id>https://play.golang.org/p/<snippet-id>
🤝 Contributing
- Ping me on Twitter @samuelberthe (DMs, mentions, whatever :))
- Fork the project
- Fix open issues or request new features
Don't hesitate ;)
Install
- Clone this repository:
git clone https://github.com/samber/go-playground-mcp.git
cd go-playground-mcp
- Install dependencies:
npm install
- Build the project:
npm run build
Running the Server
# Development mode
npm run dev
# Production mode
npm run build
npm start
Add this to your MCP client configuration:
{
"mcpServers": {
"go-playground": {
"command": "node",
"args": ["dist/index.js"]
}
}
}
👤 Contributors
💫 Show your support
Give a ⭐️ if this project helped you!
📝 License
Copyright © 2025 Samuel Berthe.
This project is MIT licensed.
FAQ
- What is the Go Playground MCP server?
- Go Playground is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Go Playground?
- This profile displays 35 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★35 reviews- ★★★★★Arjun Wang· Dec 20, 2024
Useful MCP listing: Go Playground is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Pratham Ware· Dec 16, 2024
Go Playground is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Li Smith· Dec 16, 2024
Strong directory entry: Go Playground surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Chinedu Verma· Dec 12, 2024
According to our notes, Go Playground benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Kiara Taylor· Dec 4, 2024
We wired Go Playground into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Kiara Liu· Nov 23, 2024
Go Playground reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Chinedu Menon· Nov 11, 2024
Go Playground is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Yash Thakker· Nov 7, 2024
Useful MCP listing: Go Playground is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Chinedu Robinson· Nov 7, 2024
Go Playground is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Dhruvi Jain· Oct 26, 2024
Go Playground reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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