AI tool calling and MCP: how business applications connect

1. Understand the tool-calling loop

The application defines available tools and their inputs. A model may propose a tool name and arguments. Application code checks that proposal, executes the permitted function, and returns a result for the next step or final response. The model does not directly execute the business function.

Keep the tool contract specific. A get-order-status operation can return a limited set of authorized fields. A separate cancel-order operation can enforce cancellation rules and review. Combining many unrelated actions into one vague tool makes evaluation and authorization harder.

2. Know what MCP adds

Model Context Protocol provides a standard integration pattern for an AI host to connect to servers exposing capabilities such as tools, resources, and prompts. A client connects to a server; the host decides what to make available in the user’s interaction.

MCP can reduce custom connection work, but standard communication does not make the business operation trustworthy by itself. Inspect the server, its authentication and authorization model, the capabilities it exposes, and where it executes. A local process and a remote service have different deployment and credential requirements.

3. Pass trusted identity separately from model arguments

Derive the principal from the authenticated application session. A proposed tool call can identify the requested record, but it should not be able to grant itself a different tenant or role. Enforce the record check inside the implementation.

This framework-independent Python example demonstrates a read-only boundary using invented in-memory data. The principal must come from trusted authentication in a real application. It is not a complete authentication service or an MCP server.

ORDERS = {"demo-42": {"tenant": "demo-a", "status": "ready"}}

def order_status(principal, order_id):
    order = ORDERS.get(order_id)
    if order is None or order["tenant"] != principal["tenant"]:
        raise PermissionError("Order unavailable for this account")
    return {"order_id": order_id, "status": order["status"]}

trusted_principal = {"tenant": "demo-a"}  # supplied by application auth
print(order_status(trusted_principal, "demo-42"))

4. Design execution, errors, and review

Validate required fields and business constraints after parsing arguments. Return a structured success or error result that distinguishes missing information, denied access, temporary failure, and a completed operation. Do not turn every failure into an instruction for the model to retry.

For writes, plan operation identifiers, confirmation where required, and reconciliation after uncertain network outcomes. A schema can validate shape, but it cannot decide whether a refund is allowed or a recipient is correct. Keep those checks close to the application that owns the record.

5. Evaluate each server and tool path

Test valid, malformed, denied, and repeated calls. Include users with different record access and tools that are unavailable during a request. Inspect tool selection, arguments, execution results, and the final explanation shown to the user.

Pin and document the client/server versions and supported features you deploy. Review new capabilities before exposing them to an existing assistant. The useful benefit of MCP is an understandable connection surface; reliable business behavior still comes from scoped tools, trusted permissions, and an operating process.

6. Frequently asked questions

7. Related articles and next steps

• AI agents vs chatbots vs automation: how to choose
• Prompt injection in AI applications: practical protection
• Reliable AI agent workflows: retries, approvals, and recovery
• Explore AI product development and automation services