Mcp Server Builder

MCP Server Builder is a community skill for building production-ready Model Context Protocol servers, covering server scaffolding, tool implementation, resource handlers, authentication, and deployment configuration.

What Is This?

Overview

MCP Server Builder provides patterns for constructing complete MCP servers that expose tools and resources to AI assistants. It covers project scaffolding with proper directory structure, tool handler implementation with input validation, resource endpoint creation for data access, authentication middleware, error handling strategies, and deployment configuration for both local and remote hosting. The skill enables developers to build MCP servers following established best practices.

Who Should Use This

This skill serves developers creating custom MCP integrations for specific services, teams building shared tool servers for organization-wide AI assistant access, and engineers packaging existing APIs as MCP-compatible tool endpoints.

Why Use It?

Problems It Solves

Starting an MCP server from scratch requires understanding protocol details and message formats. Tool handlers without proper input validation produce confusing errors when assistants pass unexpected arguments. Servers without authentication expose internal tools to unauthorized access. Inconsistent error handling across tools makes debugging integration issues difficult for assistant developers.

Core Highlights

Project scaffolding creates the correct file structure with transport, handler, and configuration modules. Input validation middleware checks tool arguments against JSON schemas before handler execution. Resource handlers expose read-only data endpoints that assistants can query without side effects. Authentication layer supports both API key and token-based access control.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
from typing import Any, Callable
import json

@dataclass
class ToolHandler:
    name: str
    description: str
    input_schema: dict
    handler: Callable

class MCPServerBuilder:
    def __init__(self, name: str, version: str = "1.0.0"):
        self.name = name
        self.version = version
        self.tools: dict[str, ToolHandler] = {}
        self.auth_token: str = ""

    def add_tool(self, name: str, description: str,
                 schema: dict, handler: Callable):
        self.tools[name] = ToolHandler(
            name=name, description=description,
            input_schema=schema, handler=handler)

    def set_auth(self, token: str):
        self.auth_token = token

    def validate_input(self, tool_name: str,
                       args: dict) -> list[str]:
        tool = self.tools.get(tool_name)
        if not tool:
            return [f"Unknown tool: {tool_name}"]
        errors = []
        required = tool.input_schema.get("required", [])
        for field_name in required:
            if field_name not in args:
                errors.append(f"Missing required: {field_name}")
        return errors

    def handle_request(self, request: dict) -> dict:
        method = request.get("method", "")
        if method == "tools/list":
            return {"tools": [{"name": t.name,
                "description": t.description,
                "inputSchema": t.input_schema}
                for t in self.tools.values()]}
        if method == "tools/call":
            params = request.get("params", {})
            name = params.get("name", "")
            args = params.get("arguments", {})
            errors = self.validate_input(name, args)
            if errors:
                return {"error": "; ".join(errors)}
            result = self.tools[name].handler(**args)
            return {"content": [{"type": "text",
                                 "text": str(result)}]}
        return {"error": f"Unknown method: {method}"}

Real-World Examples

from dataclasses import dataclass, field
from pathlib import Path
import json

class MCPProjectScaffolder:
    def __init__(self, project_name: str, output_dir: str):
        self.project_name = project_name
        self.output_dir = Path(output_dir)

    def scaffold(self) -> dict:
        dirs = [
            self.output_dir / "src",
            self.output_dir / "src" / "tools",
            self.output_dir / "src" / "resources",
            self.output_dir / "tests",
        ]
        files = {
            "src/server.py": f"# {self.project_name} MCP Server",
            "src/tools/__init__.py": "# Tool handlers",
            "src/resources/__init__.py": "# Resource handlers",
            "config.json": json.dumps(
                {"name": self.project_name, "version": "1.0.0"},
                indent=2),
        }
        return {"directories": [str(d) for d in dirs],
                "files": list(files.keys())}

Advanced Tips

Implement request logging middleware that records tool invocations for debugging and usage analysis. Use connection pooling for tools that access databases or external APIs to avoid creating new connections per request. Add health check endpoints that monitoring systems can use to verify server availability.

When to Use It?

Use Cases

Build an MCP server that wraps a company internal API for AI assistant access. Create a file management tool server that provides controlled directory operations. Package a database query interface as an MCP tool with schema validation and result formatting.

Related Topics

Model Context Protocol specification, API server development, tool interface design, server authentication patterns, and JSON-RPC communication.

Important Notes

Requirements

A Python runtime for server execution with JSON processing capability. Understanding of the MCP protocol message format for request and response handling. Network access configuration for the chosen transport type.

Usage Recommendations

Do: validate all tool inputs against schemas before executing handler logic. Write clear tool descriptions that help assistants understand when to use each tool. Test servers with multiple concurrent requests to verify thread safety.

Don't: expose internal error details in tool responses that could leak implementation information. Skip authentication for servers accessible over the network. Create tools with side effects that cannot be safely retried on failure.

Limitations

Stdio transport limits servers to local process communication only. Tool execution latency directly impacts assistant response time for users. Custom resource handlers must implement proper caching to avoid repeated expensive data fetches.