What real-world implementations are included in these examples?

The skill provides patterns from the lci code search tool, the agnt browser proxy, and various process management and knowledge base systems.

Does this skill help with AI agent automation?

Yes, it includes standardized automation flags like 'has_more' and 'total' that help AI agents understand when to paginate or request more detail.

What is the primary benefit of using these MCP patterns?

The primary benefit is efficiency; these patterns use ID-based referencing and progressive detail to reduce token consumption by 70-90% compared to flat data structures.

Can these patterns be applied to any MCP server?

Absolutely. While the examples focus on specific domains, the architectural principles of Hub-and-Spoke and Lazy Loading are universal for any complex MCP implementation.

MCP Implementation Patterns

Name: MCP Implementation Patterns
Author: standardbeagle

bystandardbeagle

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学習とドキュメンテーション

Provides real-world Model Context Protocol (MCP) design patterns and implementation examples to optimize AI tool efficiency and performance.

概要

This skill serves as a comprehensive reference for developers building Model Context Protocol (MCP) servers, offering battle-tested patterns derived from production systems. It focuses on critical architectural strategies such as Hub-and-Spoke discovery, token-saving ID reference systems, and progressive detail disclosure. By studying concrete implementations for code search, browser integration, and process management, developers can learn to reduce token consumption by up to 90% while significantly improving the reliability and responsiveness of their AI-driven tools.

主な機能

Proven MCP architecture patterns including Hub-and-Spoke and Lazy Loading
Real-world examples from production lci, agnt, and knowledge base servers
Progressive detail strategies that vary output based on relevance and confidence
1 GitHub stars
Standardized automation flags for enhanced AI agent compatibility
Token-efficient ID reference systems providing 70-90% data reduction

ユースケース

Implementing discovery-detail patterns for navigating large codebases or datasets
Designing robust browser proxy or process management toolsets for AI agents
Architecting high-performance MCP servers with optimized token usage

概要

主な機能

Proven MCP architecture patterns including Hub-and-Spoke and Lazy Loading
Real-world examples from production lci, agnt, and knowledge base servers
Progressive detail strategies that vary output based on relevance and confidence
1 GitHub stars
Standardized automation flags for enhanced AI agent compatibility
Token-efficient ID reference systems providing 70-90% data reduction

ユースケース

Implementing discovery-detail patterns for navigating large codebases or datasets
Designing robust browser proxy or process management toolsets for AI agents
Architecting high-performance MCP servers with optimized token usage