go-mcp-server-generator▌
github/awesome-copilot · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Generate production-ready Go MCP server projects with proper structure, dependencies, and typed tool implementations.
- ›Scaffolds complete Go module layout with official MCP SDK integration, including main server setup, tool registration, and graceful shutdown handling
- ›Provides typed tool handlers with JSON schema validation, structured inputs/outputs, and context-aware error handling
- ›Includes configuration management via environment variables, basic test structure, and README document
Go MCP Server Project Generator
Generate a complete, production-ready Model Context Protocol (MCP) server project in Go.
Project Requirements
You will create a Go MCP server with:
- Project Structure: Proper Go module layout
- Dependencies: Official MCP SDK and necessary packages
- Server Setup: Configured MCP server with transports
- Tools: At least 2-3 useful tools with typed inputs/outputs
- Error Handling: Proper error handling and context usage
- Documentation: README with setup and usage instructions
- Testing: Basic test structure
Template Structure
myserver/
├── go.mod
├── go.sum
├── main.go
├── tools/
│ ├── tool1.go
│ └── tool2.go
├── resources/
│ └── resource1.go
├── config/
│ └── config.go
├── README.md
└── main_test.go
go.mod Template
module github.com/yourusername/{{PROJECT_NAME}}
go 1.23
require (
github.com/modelcontextprotocol/go-sdk v1.0.0
)
main.go Template
package main
import (
"context"
"log"
"os"
"os/signal"
"syscall"
"github.com/modelcontextprotocol/go-sdk/mcp"
"github.com/yourusername/{{PROJECT_NAME}}/config"
"github.com/yourusername/{{PROJECT_NAME}}/tools"
)
func main() {
cfg := config.Load()
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
// Handle graceful shutdown
sigCh := make(chan os.Signal, 1)
signal.Notify(sigCh, os.Interrupt, syscall.SIGTERM)
go func() {
<-sigCh
log.Println("Shutting down...")
cancel()
}()
// Create server
server := mcp.NewServer(
&mcp.Implementation{
Name: cfg.ServerName,
Version: cfg.Version,
},
&mcp.Options{
Capabilities: &mcp.ServerCapabilities{
Tools: &mcp.ToolsCapability{},
Resources: &mcp.ResourcesCapability{},
Prompts: &mcp.PromptsCapability{},
},
},
)
// Register tools
tools.RegisterTools(server)
// Run server
transport := &mcp.StdioTransport{}
if err := server.Run(ctx, transport); err != nil {
log.Fatalf("Server error: %v", err)
}
}
tools/tool1.go Template
package tools
import (
"context"
"fmt"
"github.com/modelcontextprotocol/go-sdk/mcp"
)
type Tool1Input struct {
Param1 string `json:"param1" jsonschema:"required,description=First parameter"`
Param2 int `json:"param2,omitempty" jsonschema:"description=Optional second parameter"`
}
type Tool1Output struct {
Result string `json:"result" jsonschema:"description=The result of the operation"`
Status string `json:"status" jsonschema:"description=Operation status"`
}
func Tool1Handler(ctx context.Context, req *mcp.CallToolRequest, input Tool1Input) (
*mcp.CallToolResult,
Tool1Output,
error,
) {
// Validate input
if input.Param1 == "" {
return nil, Tool1Output{}, fmt.Errorf("param1 is required")
}
// Check context
if ctx.Err() != nil {
return nil, Tool1Output{}, ctx.Err()
}
// Perform operation
result := fmt.Sprintf("Processed: %s", input.Param1)
return nil, Tool1Output{
Result: result,
Status: "success",
}, nil
}
func RegisterTool1(server *mcp.Server) {
mcp.AddTool(server,
&mcp.Tool{
Name: "tool1",
Description: "Description of what tool1 does",
},
Tool1Handler,
)
}
tools/registry.go Template
package tools
import "github.com/modelcontextprotocol/go-sdk/mcp"
func RegisterTools(server *mcp.Server) {
RegisterTool1(server)
RegisterTool2(server)
// Register additional tools here
}
config/config.go Template
package config
import "os"
type Config struct {
ServerName string
Version string
LogLevel string
}
func Load() *Config {
return &Config{
ServerName: getEnv("SERVER_NAME", "{{PROJECT_NAME}}"),
Version: getEnv("VERSION", "v1.0.0"),
LogLevel: getEnv("LOG_LEVEL", "info"),
}
}
func getEnv(key, defaultValue string) string {
if value := os.Getenv(key); value != "" {
return value
}
return defaultValue
}
main_test.go Template
How to use go-mcp-server-generator on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add go-mcp-server-generator
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches go-mcp-server-generator from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate go-mcp-server-generator. Access the skill through slash commands (e.g., /go-mcp-server-generator) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★57 reviews- ★★★★★Kiara Sharma· Dec 28, 2024
go-mcp-server-generator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Jin Lopez· Dec 24, 2024
Registry listing for go-mcp-server-generator matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Dec 16, 2024
go-mcp-server-generator has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Noah Brown· Dec 12, 2024
Useful defaults in go-mcp-server-generator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Rahul Santra· Nov 27, 2024
Solid pick for teams standardizing on skills: go-mcp-server-generator is focused, and the summary matches what you get after install.
- ★★★★★Kwame Gupta· Nov 19, 2024
We added go-mcp-server-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Lucas Martinez· Nov 15, 2024
go-mcp-server-generator reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Piyush G· Nov 7, 2024
Keeps context tight: go-mcp-server-generator is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Noor Dixit· Nov 3, 2024
go-mcp-server-generator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Oct 26, 2024
We added go-mcp-server-generator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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