Claude integrations and MCP connectors for business software

Anthropic builds the Claude models and sells access through an API. It also created the Model Context Protocol (MCP), an open standard that lets Claude and other AI apps call tools inside other software. MCP is now governed under the Linux Foundation’s Agentic AI Foundation.

It gives your staff a new way into software you already run. A dispatcher types “what’s the status of load 4471?” into Claude, and the connector looks it up in your system and answers. An account manager asks for a client’s open orders and gets them without opening another screen. The connector can only do what your software’s API allows, and only with the permissions of the person asking.

• We use Claude in internal tooling.
• Microsive Dialer’s Claude connector, built on MCP, lets a rep place calls from Claude Desktop.

• The job has steps that use tools. Look something up, act on it, then report back, with each step calling your software.
• Your staff already work in an MCP-capable app such as Claude Desktop, and the actions they want already exist in your software’s API.
• The action can be confirmed or reversed. Placing a call suits a connector. Moving money or releasing a shipment needs far tighter controls first.
• Your approved cloud account may already offer Claude. Check whether Claude is available through the cloud account you have approved (Amazon Bedrock, Google Vertex AI or Microsoft Foundry) before starting a new vendor review.

• Thousands of short, repeated tasks where cost per call matters more than quality, such as tagging one-line status messages. Price a month of the job on a smaller hosted model, or on an open model you run yourself, before choosing.

1. Which actions can the connector take alone, and which need a person to confirm?
2. How does the connector know who the user is, and limit them to their own data?
3. Where is the record of what the AI did through the connector?
4. What data leaves our systems during a tool call?
5. What happens when the model calls the wrong tool?

• Tool names and descriptions steer the model. Vague ones lead to wrong calls, so write and test them like any other instruction.
• One shared key. A remote connector should use OAuth sign-in, so each person authorizes it with their own account and it acts with their permissions, not with a key everyone shares.
• No trail. Without a log, nobody can answer “who did this?”. Keep an audit log of every action an agent takes.

• AI automation that connects to your systems
• OpenAI voice and speech APIs