Model Context Protocol (MCP)

MCP defines how an AI application (the client) discovers and calls capabilities offered by a server. A server wraps a system such as a CRM, a database or a document store and publishes tools with names and schemas, resources the model can read, and reusable prompts. Any MCP-compatible client, whether a coding assistant, a chat product or your own agent, can then list and use them without custom integration code for each pairing.

For a business this changes the integration economics. Instead of writing a bespoke connector for every agent and every system, teams build one MCP server per system and reuse it. The server is also the natural place to enforce authentication, per-user permissions, rate limits and logging, because every call passes through it.

MCP is not an agent framework and does not decide what an agent should do; it standardises how tools are described and served. It is also not a security boundary on its own: a server that exposes a delete tool to any client with no permission check is as dangerous as any other unguarded API. The value comes when the server is treated as a governed interface, with least-privilege tool sets and audit trails.

• CTO / Head of Engineering: one server per system, reused across agents and vendors, replaces a growing pile of one-off connectors.
• CISO: MCP servers are a single choke point for authentication, permissions and logging of what AI systems do to internal tools.
• Product manager: exposing your product through MCP lets customers' assistants use it, which is becoming an integration expectation.
• Operations head: internal tools become available to assistants staff already use, without a new interface to learn.

Every agent needed its own integrations to every system, and every vendor's assistant needed its own connector to your data. MCP exists to make tool integration a standard rather than a bespoke project each time, in the way an API specification did for web services. The failure it prevents is integration sprawl that nobody can secure or maintain. The trade-off is that a standard interface makes it easier to over-expose capabilities; teams must design servers with narrow tool sets and per-user authorisation, and evaluate agents on how they use them, rather than assume the protocol handles safety.

• A SaaS company exposing its product through an MCP server so customers' AI assistants can act inside it with the user's permissions.
• A bank wrapping core-banking read operations in an internal MCP server used by several private agents, with every call logged.
• A hospital exposing scheduling and bed-status tools to internal assistants without giving any model direct database access.
• A logistics operator serving TMS and telematics tools to dispatch and customer-service agents from one governed server.
• An engineering team connecting internal wikis, ticketing and code repositories to coding assistants through MCP servers.

Standard / regulationAn open specification. Eazyware builds MCP servers over internal systems as part of private agentic AI and API development and integrations, with permission checks and audit logging inside the server rather than trusting the client.

Eazyware service that covers it: Agentic AI Solutions (self-hosted). Starting prices are on the pricing page.