AI agents are only as useful as the tools and data they can reach. Until recently, every connection between a model and a system — a CRM, a ticketing tool, a database — had to be custom-built for each application. The Model Context Protocol (MCP) changes that by giving agents a standard way to discover and use tools.
What is MCP?
MCP is an open protocol, introduced by Anthropic in late 2024 and since adopted widely across the AI industry, that standardises how AI applications connect to external tools and data. A common analogy is a USB-C port for AI: one standard connector instead of a different cable for every device.
How it works
- MCP servers expose capabilities — tools the model can call, resources it can read and prompts it can use — for a particular system, such as a database, a file store or a SaaS application.
- MCP clients live inside AI applications and agents. They connect to servers, discover what is available and pass tool calls and results between the model and the server.
- Because the interface is standard, one MCP server for, say, your ticketing system can be reused by many different agents and AI tools.
Why enterprises care
- Reuse: build an integration once and use it across assistants, agents and IDEs.
- Portability: switch models or agent frameworks without rewriting integrations.
- Ecosystem: many vendors now ship MCP servers for their products, and cloud platforms offer gateways that expose existing APIs over MCP.
The security questions to answer
MCP makes it easy to give agents powerful capabilities — which makes governance essential:
- Trust: only allow MCP servers from approved sources; review third-party servers like any other software dependency.
- Authorisation: ensure servers act with the end user’s permissions or a narrowly scoped service identity, never broad admin rights.
- Prompt injection: tool results and resources can contain malicious instructions; treat them as untrusted input.
- Least privilege: expose only the tools an agent needs; separate read and write tools; require approval for destructive actions.
- Observability: log every tool call with who, what and why.
Adopting MCP step by step
- Start with read-only MCP servers for low-risk internal knowledge sources.
- Stand up a central registry or gateway of approved MCP servers.
- Add write-capable tools with approval steps and audit logging.
- Monitor usage and review permissions regularly.
Our Agentic AI team designs MCP-based tool layers with security and governance built in.
Frequently asked questions
What is the Model Context Protocol (MCP)?
MCP is an open protocol that standardises how AI applications and agents connect to external tools, data and prompts through MCP servers and clients.
Is MCP secure?
MCP itself is a protocol; security depends on how it is deployed. Use approved servers only, scope permissions narrowly, treat tool outputs as untrusted, require approval for sensitive actions and log every call.
Do I need MCP to build AI agents?
No, but it makes integrations reusable and portable across models and agent frameworks, which becomes valuable as the number of agents and tools grows.