How do I test the MCP servers I build?

The skill includes a dedicated evaluation guide and script templates to create complex, verifiable QA pairs that test how effectively an LLM can use your server.

Does this skill support TypeScript development?

Yes, while it specializes in Python FastMCP, it provides comprehensive patterns and best practices for the official TypeScript/Node MCP SDK as well.

What are agent-centric design principles?

These are design patterns that optimize tools specifically for AI agents, focusing on workflows over raw API endpoints, context window management, and actionable error messages.

FastMCP is a high-level Python framework for building Model Context Protocol (MCP) servers using a decorator-based approach and Pydantic validation for easy integration.

FastMCP Creator

Name: FastMCP Creator
Author: Jamie-BitFlight

byJamie-BitFlight

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API Development

Build high-performance Model Context Protocol (MCP) servers using Python FastMCP and TypeScript SDKs.

About

FastMCP Creator is a comprehensive toolset for building robust Model Context Protocol (MCP) servers, specializing in the Python FastMCP framework. It guides developers through the entire lifecycle of MCP development—from agent-centric design principles and API research to implementation with Pydantic/Zod validation and production deployment. By focusing on workflow-based tool design and automated evaluations, it ensures that your AI agents have high-signal, actionable tools that function reliably in complex production environments.

Key Features

11 GitHub stars
Automated evaluation framework for testing server quality with realistic scenarios
Built-in validation patterns using Pydantic (Python) and Zod (TypeScript)
Enforces agent-centric design principles for optimized AI tool performance
Comprehensive 4-phase workflow covering research, planning, implementation, and testing
Deep specialization in Python FastMCP (decorator-based) and TypeScript MCP SDKs

Use Cases

Designing agent-optimized toolsets that prioritize high-signal data over raw API dumps
Creating evaluation harnesses to verify the reliability of complex MCP tool interactions
Building custom Python-based MCP servers to connect proprietary APIs to Claude

About

Key Features

11 GitHub stars
Automated evaluation framework for testing server quality with realistic scenarios
Built-in validation patterns using Pydantic (Python) and Zod (TypeScript)
Enforces agent-centric design principles for optimized AI tool performance
Comprehensive 4-phase workflow covering research, planning, implementation, and testing
Deep specialization in Python FastMCP (decorator-based) and TypeScript MCP SDKs

Use Cases

Designing agent-optimized toolsets that prioritize high-signal data over raw API dumps
Creating evaluation harnesses to verify the reliability of complex MCP tool interactions
Building custom Python-based MCP servers to connect proprietary APIs to Claude