“Design your MCP servers to be narrowly focused, exposing specific and granular tools to your AI agents, instead of trying to be a general-purpose API,” says Simon Margolis, associate CTO of AI and ML at SADA, an Insights Company. “This makes it easier for the AI’s reasoning engine to discover the right tool dynamically and improves the reliability of the actions it takes. An MCP server acts as a smart adapter, translating the AI’s request into the exact command the underlying tool understands.”
“We’ve found that simple, explicit instructions, such as telling the model how to use a vendor’s command-line utility, can outperform a poorly integrated MCP server,” adds Andrew Filev, CEO and founder of Zencoder. “Overloading the model’s context with too many MCP tools can actually degrade performance, confuse the agent, and obscure reasoning paths.”
Creating separate servers for finance, HR, customer support, and IT simplifies creating access rules, monitoring operations for anomalies, and defining lifecycle management policies.
