How do I verify if my MCP server works correctly?

The skill includes a dedicated evaluation phase where you create complex, realistic QA pairs in XML format to test the LLM's ability to solve problems using your tools.

Which programming languages are supported by mcp-builder?

The skill provides detailed implementation patterns for both TypeScript (using the MCP Node SDK) and Python (using the Python SDK and FastMCP).

What is the Model Context Protocol (MCP)?

MCP is an open standard that enables LLMs to safely and efficiently interact with external data sources and services through a standardized set of tools and resources.

Does it support remote MCP server deployment?

Yes, it provides guidance for both local stdio-based servers and remote servers utilizing streamable HTTP transport for scalability.

How does this skill improve tool discoverability for AI?

It enforces best practices such as consistent action-oriented naming (e.g., github_create_issue), concise descriptions, and specific annotations like readOnlyHint to help agents select the right tool.

MCP Server Builder

Name: MCP Server Builder
Author: partme-ai

bypartme-ai

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API Development

Guides developers through the end-to-end creation of high-quality Model Context Protocol (MCP) servers for both Python and TypeScript environments.

About

The mcp-builder skill is a comprehensive development companion designed to help you architect, implement, and test Model Context Protocol (MCP) servers. It provides domain-specific guidance for creating robust tool integrations that allow LLMs to interact seamlessly with external APIs and services. By following industry best practices for naming, schema design, and error handling, this skill ensures your MCP servers are highly discoverable and reliable for AI agents. It covers the entire lifecycle from initial API research and infrastructure setup to the creation of rigorous evaluation sets to verify tool performance.

Key Features

Strategic planning for API coverage, tool discoverability, and context management
Cross-platform support for TypeScript (Node.js) and Python (FastMCP) SDKs
Standardized project structure setup for both local (stdio) and remote (HTTP) transports
Integrated evaluation framework for testing LLM tool-calling accuracy using XML-based QA pairs
Guided tool design using Zod or Pydantic for strict schema validation
37 GitHub stars

Use Cases

Building custom API connectors to give Claude access to internal company databases or proprietary tools
Prototyping and testing new MCP protocol implementations with standardized evaluation metrics
Developing specialized workflow automations that bridge third-party SaaS platforms and AI agents

About

Key Features

Strategic planning for API coverage, tool discoverability, and context management
Cross-platform support for TypeScript (Node.js) and Python (FastMCP) SDKs
Standardized project structure setup for both local (stdio) and remote (HTTP) transports
Integrated evaluation framework for testing LLM tool-calling accuracy using XML-based QA pairs
Guided tool design using Zod or Pydantic for strict schema validation
37 GitHub stars

Use Cases

Building custom API connectors to give Claude access to internal company databases or proprietary tools
Prototyping and testing new MCP protocol implementations with standardized evaluation metrics
Developing specialized workflow automations that bridge third-party SaaS platforms and AI agents