What is the primary purpose of the MCP Architecture skill?

It provides standardized patterns and best practices for building servers using the Model Context Protocol, ensuring they are secure, efficient, and well-structured.

Does this skill support both Python and TypeScript?

Yes, it includes implementation examples and architectural guidance for both Python (via FastMCP) and TypeScript environments.

Can I use this to optimize token usage?

Absolutely. It includes context window optimization techniques like schema compaction and session memory management to maximize efficiency within LLM limits.

How does this skill handle MCP security?

It provides specific defenses against tool poisoning, shadowing detection, sandboxing strategies for untrusted code execution, and strict input validation patterns.

MCP Architecture & Security

Name: MCP Architecture & Security
Author: jpoutrin

byjpoutrin

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API Development

Architects, implements, and secures Model Context Protocol (MCP) servers using advanced patterns for memory management and tool execution.

About

This skill provides a deep technical foundation for building production-grade Model Context Protocol (MCP) servers in Python and TypeScript. It covers the essential Client-Host-Server model and transport protocols while offering specialized guidance on MCP primitives like resources, tools, and sampling. Beyond basic setup, the skill focuses on robust operational standards, including security patterns to prevent tool poisoning, multi-tier caching for memory management, and context window optimization. It is an essential resource for developers looking to create secure, efficient, and scalable integrations between LLMs and external data sources or tools.

Key Features

Implementation patterns for MCP primitives including Tools, Resources, and Prompts
Multi-tier caching strategies spanning in-memory, Redis, and SQLite
Security protocols for tool poisoning prevention and cross-server shadowing detection
Context window optimization through automated schema compaction and message pruning
4 GitHub stars
Server lifecycle management including graceful shutdown and request tracking

Use Cases

Securing AI-driven tool execution through sandboxing and input validation
Designing and debugging custom MCP servers using FastMCP for Python or TypeScript
Optimizing LLM performance in long-running sessions with tiered memory management

About

Key Features

Implementation patterns for MCP primitives including Tools, Resources, and Prompts
Multi-tier caching strategies spanning in-memory, Redis, and SQLite
Security protocols for tool poisoning prevention and cross-server shadowing detection
Context window optimization through automated schema compaction and message pruning
4 GitHub stars
Server lifecycle management including graceful shutdown and request tracking

Use Cases

Securing AI-driven tool execution through sandboxing and input validation
Designing and debugging custom MCP servers using FastMCP for Python or TypeScript
Optimizing LLM performance in long-running sessions with tiered memory management