Model Context Protocol (MCP)

AI assistants and agents become far more useful when they can reach beyond their training data. An assistant that can read a company's documents, query its databases, check its calendar or create a ticket in its work management system can do real work rather than just answer general questions. Before common standards existed, every AI application needed custom code to connect to every tool, and every tool vendor had to build separate integrations for each AI product. The Model Context Protocol was created to solve this. It defines one standard way for AI applications to discover and use tools and data, so a single integration can work across many AI products.

Anthropic introduced MCP as an open standard in November 2024, and it was adopted quickly across the industry, with support added by other major AI providers, developer tools and software vendors during 2025. In late 2025, stewardship of the protocol moved to the Agentic AI Foundation under the Linux Foundation, alongside other open agent projects. MCP is often compared to a universal port for AI: instead of a different connector for every pairing of AI application and tool, both sides speak the same protocol. For software vendors, platform teams, AI developers, security teams and technology leaders, MCP has become a key part of how AI connects to enterprise systems.

Definition of Model Context Protocol (MCP)

The Model Context Protocol is an open, client-server protocol that standardizes how AI applications, acting as hosts with MCP clients, connect to MCP servers that expose tools, resources and prompts from external systems, so that language models can discover available capabilities, retrieve context and invoke actions in a consistent, permissioned way.

It is an API-first approach for AI, complements the Agent2Agent Protocol (A2A) and is a common building block for AI agents.

Core Components of MCP

Hosts. AI applications, such as chat assistants, coding tools or agent platforms, that users interact with.

Clients. Components within the host that manage connections to MCP servers.

Servers. Programs that expose a system's capabilities, such as a database, file store, SaaS application or internal service, through the protocol.

Tools, resources and prompts. Servers can offer tools that the model can call to take actions, resources that provide data or content as context and prompt templates for common tasks.

Transports. Connections can run locally on a user's machine or remotely over HTTP, with authorization for remote servers.

MCP vs. Traditional APIs

Audience. Traditional APIs are designed for developers who write code against them, while MCP servers describe capabilities in a form AI models can discover and use directly.

Discovery. MCP clients can ask a server what tools and resources it offers at run time, rather than relying on hard-coded integrations.

Reuse. One MCP server can be used by many different AI applications that support the protocol.

Relationship. MCP servers usually sit on top of existing APIs, translating them for AI use rather than replacing them. Well-designed APIs therefore make it much easier to build good MCP servers.

Importance of MCP in Enterprise Operations

It speeds AI integration. Organizations can connect AI tools to internal systems once rather than building custom integrations for each AI product.

It reduces lock-in. Because MCP is an open standard, organizations can change AI providers without rebuilding every connection.

It also makes enterprise data usable by AI. Agents can work with current, authoritative information from business systems rather than relying on what they learned in training. This reduces outdated or invented answers and lets responses cite the systems they came from.

MCP and Financial Management

Standard connectors reduce the development and maintenance costs of integrating AI with business systems.

Software vendors that offer MCP servers make their products easier to use with AI, which can affect purchasing decisions.

Usage of AI tools through MCP can drive model and API costs, which should be monitored as part of AI cost management. Large tool outputs can consume many tokens, so servers should return only the data needed.

Security reviews and governance for MCP servers add effort that should be planned as part of AI programs.

Common Challenges

Security risks, such as prompt injection through tool outputs or overly broad permissions, must be managed carefully.

Organizations lack an inventory of which MCP servers are in use and what access they grant. Employees may install servers on their own, creating a form of shadow AI that security teams cannot see.

Poorly designed servers expose too many tools or unclear descriptions, confusing models and leading to wrong actions. A smaller number of well-described tools usually works better than exposing every function of a system.

Quality and trustworthiness of third-party servers vary widely. Organizations should review third-party servers as they would any other software that touches their data.