Model Context Protocol (MCP) is emerging as a standard protocol for federating tool calls between agents. Enterprises are starting to adopt MCP as a type of microservice architecture for teams to reuse each other's tools across different AI applications.
But there are real risks with using MCP tools in production agents. Tool names, descriptions, and argument schemas become part of your agent's prompt and can change unexpectedly without warning. This can lead to security, cost, and quality issues even when the upstream MCP server has not been compromised or is not intentionally malicious.
We built mcp-to-ai-sdk to reduce these issues. It is a CLI that generates static AI SDK tool definitions from any MCP server. Definitions become part of your codebase, so they only change when you explicitly update them.