mcp-architecture_skill

Overview

This skill teaches how to design and implement Model Context Protocol (MCP) servers to standardize AI-to-data and AI-to-tool integrations. It focuses on the three MCP capabilities—resources, tools, and prompts—plus security, observability, and testing patterns for production-ready servers. You’ll get practical guidance for Python and TypeScript servers, plus decision criteria for when to build versus reuse servers.

How this skill works

An MCP server exposes resources (readable data), tools (callable functions), and prompts (reusable templates) over a JSON-RPC transport so any compliant agent can access them. Servers register resource handlers, tool handlers with input schemas and scopes, and prompt templates; clients discover capabilities and call methods to read resources, invoke tools, or run prompts. Production patterns include auth, input validation, rate limiting, caching, streaming, logging, and metrics.

When to use it

• You need a reusable, vendor-agnostic integration for AI agents to access your data or services.
• Multiple agent implementations must interact with the same data sources without bespoke adapters.
• You want a standardized interface for third parties to call domain tools securely.
• Prototyping AI workflows quickly by reusing an existing MCP server (GitHub, Slack, Postgres).

Best practices

• Define clear resource URIs and mimeTypes to let agents fetch the right format.
• Use JSON Schema for tool inputs and validate/sanitize every call to prevent injection.
• Implement OAuth2 scopes, enforce required_scope per tool, and rotate credentials.
• Add rate limiting, caching, and streaming for large datasets to protect backend systems.
• Instrument tools with metrics and audit logs for observability and post-incident review.
• Write unit and integration tests with mock MCP transports to validate workflows.

Example use cases

• Expose customer records and analytics as resources and a query tool for agent-driven support tickets.
• Create a GitHub MCP server that lists issues, creates PRs, and provisions CI jobs.
• Aggregate data from CRM, support, and analytics into an aggregated:// resource for holistic insights.
• Build a Slack MCP server that surfaces channels and lets agents post notifications via a tool.
• Provide a streaming tool to let agents process large query results without loading everything in memory.