Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Explore the Model Context Protocol specification from the ground up — JSON-RPC 2.0 message format, request/response lifecycle, transport layers, and how every MCP message is structured under the hood.
Build a production-ready MCP server in Python using FastMCP that exposes database query and mutation tools to any AI agent, complete with input validation, error handling, and async database access.
Create a fully typed MCP server in TypeScript using the official MCP SDK, with tool handlers, Zod validation, and deployment strategies for exposing Node.js services to AI agents.
Learn how MCP resources differ from tools, how to define resource URIs and templates, expose read-only data to AI agents with proper content types, and implement pagination for large datasets.
Master MCP prompts — server-defined instruction templates that guide AI agent behavior with dynamic arguments, context-aware instructions, and reusable prompt patterns across multiple agents.
Learn how MCP clients discover available servers, understand server manifests and tool catalogs, and build a lightweight server registry that lets agents dynamically connect to the right tools.
Build and deploy MCP servers accessible over HTTP using the Streamable HTTP transport, with Server-Sent Events for streaming, stateless vs stateful session modes, and strategies for horizontal scaling.
Design and implement multi-server MCP architectures where agents connect to multiple tool providers simultaneously, with namespace management, conflict resolution, and performance optimization.
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