What programming languages does the MCP Server Builder skill support?

This skill provides specific implementation guides for both TypeScript (using the official MCP SDK) and Python (using the FastMCP framework).

Can I use this to wrap existing REST APIs?

Yes, the skill includes patterns for creating API clients with authentication, error handling, and pagination support to bridge traditional APIs with the MCP standard.

How does this skill help with tool discoverability?

It provides best practices for action-oriented tool naming, clear parameter descriptions, and context management strategies that help LLMs identify the right tool for the job.

Does it include a way to test my MCP server?

Yes, it includes a dedicated Evaluation Phase (Phase 4) with a framework for creating complex, verifiable QA pairs to test how well an LLM uses your tools.

MCP Server Builder

Name: MCP Server Builder
Author: icartsh

byicartsh

API Development

Provides a comprehensive framework and guided workflow for developing high-quality Model Context Protocol (MCP) servers to connect LLMs with external services.

About

MCP Server Builder is a specialized skill designed to streamline the creation of Model Context Protocol (MCP) servers using TypeScript or Python. It guides developers through the entire lifecycle of MCP development—from deep research and API planning to implementation with robust error handling and automated evaluation. By following structured patterns for tool naming, context management, and schema definition, this skill ensures that external services are integrated in a way that is highly discoverable and reliable for AI agents, allowing Claude to interact seamlessly with custom APIs and databases.

Key Features

Implementation patterns for robust schema validation using Zod and Pydantic.
Framework for creating XML-based evaluations to measure LLM tool-calling effectiveness.
Step-by-step workflow for TypeScript (MCP SDK) and Python (FastMCP) development.
Guidance on balancing API coverage with specialized high-level workflow tools.
0 GitHub stars
Standardized error handling strategies to provide actionable feedback to AI agents.

Use Cases

Building a custom MCP server to connect internal company APIs to Claude Code.
Creating automated test suites to verify the reliability of complex multi-step tool workflows.
Standardizing existing API integrations into the Model Context Protocol format for better AI discoverability.

About

Key Features

Implementation patterns for robust schema validation using Zod and Pydantic.
Framework for creating XML-based evaluations to measure LLM tool-calling effectiveness.
Step-by-step workflow for TypeScript (MCP SDK) and Python (FastMCP) development.
Guidance on balancing API coverage with specialized high-level workflow tools.
0 GitHub stars
Standardized error handling strategies to provide actionable feedback to AI agents.

Use Cases

Building a custom MCP server to connect internal company APIs to Claude Code.
Creating automated test suites to verify the reliability of complex multi-step tool workflows.
Standardizing existing API integrations into the Model Context Protocol format for better AI discoverability.