agency-mcp-builder

Building an MCP server involves designing strongly-typed custom tools, resources, and prompts in TypeScript or Python. The server must enforce statelessness, robust error handling, and secure credential handling to ensure reliable AI agent behavior.

The Model Context Protocol is used to extend AI agents with custom tools, resources, and prompts. It provides a scalable, stateless interface for agents to access context resources reliably during task execution.

Yes, MCP tool development supports both TypeScript and Python for implementing and testing servers. You can define well-named, strongly-typed tools with clear usage guidance in either language to expose resources and prompts to agents.

Best practices for MCP server security include enforcing statelessness, implementing robust error handling, and applying secure credential handling. These production-grade patterns ensure reliable integrations and predictable agent decision-making.

You expose resources and prompts by defining them within your MCP server architecture. Providing agent-visible resources informs decision-making by giving the model structured access to necessary context during execution.

You should use a stateless tool interface for API integration when you need scalable and reliable agent behavior. Stateless interfaces ensure consistent performance and robust error handling without retaining session data between calls.