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AI & Agents

MCP (Model Context Protocol)

MCP is an open standard for exposing tools and data to AI agents through a consistent interface. Building one MCP server for a system means any MCP-compatible agent or client can use it, instead of writing a separate connector for each assistant an organisation adopts.

The value MCP delivers is reduced integration maintenance rather than new capability. Anything an MCP server does could be built as a direct integration; the difference is that a direct integration serves one client, while an MCP server serves every compatible one. As the number of agents and assistants inside an organisation grows, that difference compounds.

It is not always the right choice. For a single agent talking to a handful of internal endpoints, direct function calling is simpler and has fewer moving parts. MCP earns its keep when the same capability must be reachable from several agents, when you want a clean boundary between the agent team and the system owners, or when the agent landscape is expected to keep changing.

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MCP (Model Context Protocol)
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Direct function calling is simpler and sufficient when one agent talks to a few internal endpoints. MCP earns its cost when the same capability needs to be reachable from several agents or clients, when you want a clean interface boundary between teams, or when you expect the set of assistants in use to keep changing. The answer depends on scale, not on novelty.

MCP is a protocol, so security comes from how the server is built. The controls that matter are the same as for any agent tool layer: least-privilege credentials per tool, short-lived tokens, server-side rate limits, and treating any content returned by a tool as untrusted data rather than instructions. A well-built MCP server is exactly as safe as its narrowest permission.