MCP, Explained (How AI Agents Connect to Tools)

AI agents can do quite a bit more than generate text. They can retrieve information, interact with APIs, create tickets, and even execute code. But for any of that to happen, the agent needs a way to communicate with the systems it's supposed to work with.

Traditionally, developers had to build custom integrations for each application and service. Connecting an AI assistant to GitHub, Jira, or an internal database meant implementing the necessary APIs, authentication, and tool definitions individually.

The Model Context Protocol, or MCP, provides a standardized way for AI applications to connect with external tools and information sources.

The Basics

MCP (Model Context Protocol) is an open protocol that defines how AI applications communicate with external systems. It was introduced by Anthropic and is designed to make integrations more consistent across different AI applications and services.

MCP uses a client-server architecture. An AI application acts as an MCP host and creates clients that communicate with MCP servers. These servers expose capabilities such as tools, which perform operations, resources, which provide information, and prompts, which offer reusable interaction templates.

For example, a GitHub MCP server might expose tools for retrieving repository information, reviewing issues, or creating pull requests. An AI agent connected to that server can discover the available tools and request their execution without requiring developers to implement a separate integration for every operation.

Why It Exists

Without a common protocol, integrating AI applications with external services can become difficult to maintain. Every application may need its own implementation for discovering tools, describing their inputs, and processing their outputs.

MCP establishes a common interface for these interactions. Developers can build an MCP server around an existing API, allowing compatible AI applications to access its capabilities through a consistent protocol. The underlying API still exists, but the integration becomes easier to reuse across different AI applications.

This is particularly useful as organizations begin experimenting with multiple models, agent frameworks, and development tools. Instead of rebuilding integrations for each environment, teams can expose common capabilities through MCP servers and connect them to supported clients.

Common Pitfalls

• Assuming MCP replaces APIs: MCP provides a standardized interface to tools and data sources, but the underlying services still rely on their existing APIs.
• Overlooking authentication: MCP connections still require appropriate credentials and access controls.
• Exposing too many tools: Giving an agent access to unnecessary operations increases complexity and security risk.
• Trusting tool descriptions blindly: Malicious or compromised servers can expose misleading instructions or tool definitions.
• Ignoring execution boundaries: An MCP server may have access to systems or data that the AI application itself shouldn't be able to modify.
• Assuming compatibility guarantees reliability: A server supporting MCP doesn't automatically mean its underlying tools will behave correctly.

Why It Matters

MCP makes it easier to connect AI applications with the systems organizations already use. A development assistant could access GitHub repositories, retrieve Jira tickets, and search internal documentation through separate MCP servers. An infrastructure agent could use tools to retrieve monitoring data, inspect cloud resources, or query operational databases.

MCP servers can run locally alongside an application or remotely as network-accessible services. Local integrations commonly use standard input and output for communication, while remote deployments can use Streamable HTTP. The appropriate approach depends on where the application runs and which systems it needs to access.

For cloud deployments, remote MCP servers introduce familiar infrastructure requirements, including networking, authentication, availability, logging, and scaling. An MCP server handling requests from multiple agents may need to support concurrent operations, manage credentials securely, and enforce limits on access to downstream services.

The TAM Lens

From an infrastructure perspective, MCP servers are another application component that needs to be deployed and managed. Their resource requirements depend on the operations they expose. A server that forwards lightweight API requests may need relatively little compute, while one performing database queries or processing large datasets could require additional resources.

Security is particularly important when MCP servers expose operational capabilities. A read-only integration for retrieving Kubernetes logs has a different risk profile from one capable of deleting resources or modifying production configurations. Authentication, authorization, network restrictions, and audit logging should be considered before making these tools available to agents.

There's also the question of reliability. An agent workflow involving several MCP servers depends on the availability of those services and the APIs behind them. Timeouts, rate limits, and failed tool calls need to be handled appropriately, especially when agents are performing multi-step tasks.

How to Stay Sane

• Start with a limited set of tools: Expose only the capabilities required for the intended workflow.
• Use least-privilege credentials: Restrict each MCP server's access to the systems it needs.
• Separate sensitive operations: Apply additional controls to tools that modify infrastructure or data.
• Validate MCP servers: Review server implementations and their dependencies before deployment.
• Monitor tool execution: Track requests, failures, latency, and downstream API usage.
• Plan for availability: Consider how agent workflows behave when an MCP server or external service becomes unavailable.

Final Thoughts

MCP provides a common way for AI applications to interact with external tools and information sources. It reduces the need for application-specific integrations and makes it easier to reuse existing capabilities across different AI environments.

For organizations building AI agents, MCP can simplify how those agents connect to operational systems. The protocol standardizes the connection, while the surrounding infrastructure still needs to handle security, reliability, and access control.