Can I use this for existing MCP tools?

Yes, the skill is ideal for refining and auditing existing tool schemas to improve their reliability, validation, and error reporting.

What is the Model Context Protocol (MCP)?

MCP is an open standard that enables developers to build secure, two-way integrations between their data sources and AI models like Claude.

How does this skill optimize token usage?

It suggests omitting redundant input data in responses and using progressive detail patterns to provide only the most relevant information based on confidence scores.

What are the different tool types supported?

It provides specific patterns and token budgets for Query (fast discovery), Lookup (detailed information), and Analysis (deep/expensive) tools.

How does it help with error handling?

It provides a standardized structure for error responses including specific codes, schema hints for the LLM, and actionable next steps to guide the user toward success.

MCP Tool Architect

Name: MCP Tool Architect
Author: standardbeagle

bystandardbeagle

•

API 开发

Streamlines the creation of robust Model Context Protocol tools with standardized schemas and intelligent error handling.

关于

MCP Tool Architect provides expert guidance for designing high-quality Model Context Protocol (MCP) tools, ensuring they are token-efficient, user-friendly, and technically sound. It assists developers in defining clear input and output schemas, implementing standardized error codes, and utilizing progressive detail patterns to optimize AI interactions. By following these established best practices, developers can create tools that offer superior client guidance, graceful degradation, and improved reliability across the entire MCP ecosystem.

主要功能

Standardized input and output schema generation
Progressive detail patterns for varying data relevance
Comprehensive error handling and status code mapping
1 GitHub stars
Token-efficient response optimization strategies
Actionable client guidance and auto-correction suggestions

使用场景

Refining existing tool schemas for better performance and lower token consumption
Designing a new MCP tool from scratch with proper validation and schema documentation
Implementing advanced error handling patterns that guide AI models toward successful recovery

关于

主要功能

Standardized input and output schema generation
Progressive detail patterns for varying data relevance
Comprehensive error handling and status code mapping
1 GitHub stars
Token-efficient response optimization strategies
Actionable client guidance and auto-correction suggestions

使用场景

Refining existing tool schemas for better performance and lower token consumption
Designing a new MCP tool from scratch with proper validation and schema documentation
Implementing advanced error handling patterns that guide AI models toward successful recovery