What programming languages does this skill support?

This skill provides detailed implementation guidance and best practices for both TypeScript (using the MCP SDK) and Python (using FastMCP).

Does it cover local and remote server configurations?

Yes, the skill includes guidance for both stdio transport (commonly used for local servers) and streamable HTTP for remote server deployments.

How does this help with tool discoverability?

It provides standardized naming conventions and description patterns that help LLMs more accurately identify and select the correct tool for specific tasks.

How do I verify the quality of my MCP server using this skill?

The skill includes a dedicated evaluation phase that guides you through creating complex, realistic QA pairs to test the effectiveness of your tools.

MCP Server Builder

Name: MCP Server Builder
Author: PedroGiudice

byPedroGiudice

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API Development

Guides the development of high-quality Model Context Protocol (MCP) servers to connect LLMs with external APIs and services.

About

The MCP Server Builder skill provides a comprehensive framework for creating robust MCP servers using TypeScript or Python. It streamlines the entire development lifecycle, from deep research and API planning to tool implementation and rigorous evaluation. By following specialized patterns for tool naming, context management, and actionable error handling, this skill ensures that the resulting servers are highly discoverable and effective for AI agents, enabling seamless interaction between LLMs and external data sources.

Key Features

Step-by-step implementation workflows for TypeScript and Python
Best practices for tool naming and discoverability
Comprehensive evaluation framework with QA pair generation
Reference documentation for transport mechanisms and SDKs
1 GitHub stars
Guidelines for structured data output using Zod and Pydantic

Use Cases

Modernizing legacy service wrappers for AI agent compatibility
Creating standardized evaluation sets to measure MCP tool performance
Building a custom MCP server to integrate a private corporate API with Claude

About

Key Features

Step-by-step implementation workflows for TypeScript and Python
Best practices for tool naming and discoverability
Comprehensive evaluation framework with QA pair generation
Reference documentation for transport mechanisms and SDKs
1 GitHub stars
Guidelines for structured data output using Zod and Pydantic

Use Cases

Modernizing legacy service wrappers for AI agent compatibility
Creating standardized evaluation sets to measure MCP tool performance
Building a custom MCP server to integrate a private corporate API with Claude