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mcp-builder

Build MCP servers with TypeScript and Python SDKs.

10|2|Updated Mar 10, 2026
Search Tags:#prompt engineering#sdk#mcp#api integration#ai tools#server development
One-click install
npx skills add https://github.com/bugrabilge/bilge-development-kit --skill mcp-builder-bugrabilge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/bugrabilge/bilge-development-kit/tree/main/skills/mcp-builder
Command: npx skills add https://github.com/bugrabilge/bilge-development-kit --skill mcp-builder-bugrabilge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of building, integrating, and managing Model Context Protocol (MCP) servers, enabling AI agents to interact with external services and data through a standardized protocol.

Core Features & Use Cases

  • MCP Server Development: Provides guidance and code examples for setting up MCP servers using TypeScript and Python SDKs.
  • Tool, Resource, and Prompt Definition: Demonstrates how to define callable tools, readable resources, and reusable prompt templates with clear descriptions and schemas.
  • Transport Options: Explains different communication methods like stdio, SSE, and streamable HTTP.
  • Use Case: You need to expose a custom API as a tool that an AI assistant can call. This Skill guides you through defining the tool, its inputs, and how to integrate it into an MCP server.

Quick Start

Use the mcp-builder skill to create a basic MCP server in TypeScript that exposes a 'get-weather' tool.

Frequently Asked Questions about mcp-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an MCP server to expose custom tools for AI agents?▼

Build an MCP server using TypeScript or Python SDKs to define callable tools with Zod schemas, expose readable resources via URIs, and create reusable prompt templates for AI agent integration.

What transports can I use for remote MCP server deployment?▼

MCP servers support stdio for local deployments, as well as SSE and streamable HTTP transports for remote deployments, allowing flexible communication between AI agents and external services.

How does Model Context Protocol integrate external APIs with AI assistants?▼

Model Context Protocol integrates external APIs by defining them as callable tools within an MCP server, enabling AI agents to discover and invoke external capabilities through a standardized protocol.

Can I define reusable prompt templates in an MCP server?▼

Yes, MCP servers support prompt templating for composable AI services, allowing you to define reusable prompt templates alongside callable tools and readable resources with clear descriptions and schemas.

What is the best way to expose a custom API as a tool for an AI assistant?▼

Expose a custom API by defining it as a callable tool within an MCP server, specifying its inputs with Zod schemas and clear descriptions so AI agents can discover and invoke the external capability.

Do I need Zod schemas to define tools in an MCP server?▼

Zod schemas are used to define callable tools with clear descriptions and input validation in MCP servers, ensuring AI agents can properly discover and invoke external capabilities through the standardized protocol.

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