Live call features
Connect your AI agent to external systems via an MCP server. This way it can use live data such as CRM information or calendar availability directly during the conversation.
How do I set up live call functions for the AI agent?
For some use cases, your AI agent needs current data from external systems during an ongoing call—for example, customer data from your CRM or live availability from your calendar. Live call functions make exactly that possible: they connect your AI agent to a system of your choice via an MCP server.
MCP stands for Model Context Protocol — a standardized interface that allows AI agents to communicate with external services.
Prerequisite: You need an accessible MCP server.
Step 1: Add MCP server
Open the desired AI agent in app.sipgate.com.
Switch to the Integrations.
tab Click in the on Add.
Fill out the form:
Name (optional): An internal display name for the server.
URL: The HTTPS address of your MCP server.
Token (optional): An authentication token, if your server requires protection.
Click Save. sipgate connects to the server automatically and checks the connection.
Warning: The URL must start with https:// . Connections without TLS are not accepted.
Step 2: Check MCP server tools
After a successful connection, sipgate displays the tools provided by the server in the overview. Check there:
Tool name: May be at most 28 characters long. Longer names are marked as errors in the overview and may cause the AI agent not to use the tool reliably.
Tool descriptions: The descriptions are received from the MCP server and adopted. The more precise the description, the better the AI agent can recognize when to use the tool. If possible, adjust the tool description on the MCP server side.
Parameters: Each parameter should also have a meaningful description. The information about which format (string, array, etc.) is expected by the MCP server and whether it is optional or required is crucial for a successful exchange between the agent and the MCP server.
Tip: The description is the most important part of a tool—it is the only thing the agent uses to infer when and how to use the tool.
A good description:
clearly states the purpose of the tool
explains, if applicable, limitations (e.g. maximum 3 results, weekdays only)
avoids technical implementation details (what happens behind the scenes is irrelevant to the agent)
A bad description is too general or missing altogether. The agent will then use the tool incorrectly or not at all.
Step 3: Integrate MCP server into your playbook integrate
The AI agent works toward goals. Describe in the playbook, what it should achieve — not how it should proceed step by step.
Good: “Determine the caller’s customer number based on their phone number get_customer and greet them by name get_name."
Less good: “1. Call the tool get_customer up. 2. Save the ID. 3. Then call get_name up …"
The AI agent decides for itself which tools it uses in which order to achieve the goal.
Warning: Business logic, validation, and state management do not belong in the playbook—that is the MCP server’s job. The playbook describes goals; the MCP server provides the tools.
Example: playbook-structure for an appointment booking
Conditions for the playbook: If the user wants to book an appointment or asks about available appointments.
1
Ask for time of day
Ask the user which part of the day they prefer for the appointment. The four options are: morning, noon, afternoon or evening. Remember the answer as preferred_timeslot for all further tool calls.
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2
Get appointment suggestions
Call get_next_suggestions with the remembered preferred_timeslot value. Present the returned appointment suggestions to the user with date and time.
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3
Confirm slot or alternative
Ask the user: Does one of the suggestions fit? Would they like to specify a particular day? Or does none of the suggestions fit? • Slot chosen → remember appointment, continue with Task 5 (capture first name) • Specific day → continue with Task 4 (ask for day & daily suggestions) '• None fit → back to Task 2 (get appointment suggestions)
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4
Ask for day & daily suggestions
Ask for the desired date. Call get_suggestions_for_day and present the suggestions. Return to the logic of Task 3.
Error handling:
• invalid date format → Explain to the user that the date was not recognized and ask again for a valid date (format: DD.MM.YYYY)
• date too far in the future → Inform the user that booking is only possible up to 2 months in advance and ask for a closer date
• Other error → Explain the problem and return to Task 3 (confirm slot or alternative) so the user can choose another day or request new suggestions
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5
Capture first name
IF the first name is not yet known: ask specifically for it. IF already known: skip this task.
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6
Capture last name
IF the last name is not yet known: ask specifically for it. IF already known: skip this task.
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7
Capture phone number
The caller’s phone number is in the context under “User with the phone number: …” — use this directly, DO NOT ask for it. Only if this entry is missing or the caller is anonymous: ask specifically for the phone number.
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8
Confirm contact details
Read out first name, last name, and phone number again and ask for confirmation. If the user makes a correction: apply the correction and read the data again.
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9
Book appointment
Call book_appointment with the appointment data (start, end) and the contact details (firstname, lastname, phone). IF booking successful (success: true): Confirm the booking with date and time and continue with Task 10 (thank and say goodbye).
Error handling:
• Slot taken (409 / “no longer available”) → Inform the user that the slot has now been taken and return to Task 2 (get appointment suggestions)
• Other error → Briefly explain to the user that something went wrong and ask whether they would like to choose another appointment (back to Task 3 – confirm slot or alternative) or try again later
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10
Thank and say goodbye
Thank the user for the conversation and wish them a nice day. Do not make any further offers.
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Update connection
If you have updated your MCP server (e.g. added new tools), click in your agent’s live call functions overview under Integrations on the button ... → Refresh and the current tool list will be retrieved again.
Test live call functions
Manual test calls are recommended, where the playbook is actively used. Check:
Does the agent trigger the right tools?
Are the parameters filled in correctly?
Does the agent respond sensibly to server error messages?
Security notes
When setting up your MCP server, note the following points:
Access rights: If your MCP server runs with admin rights on a third-party system, the AI agent potentially has extensive access. Grant only the minimum permissions necessary.
Identity verification: Consider which information a caller can request—and whether additional authentication for sensitive operations makes sense.
Input validation: Validate all inputs server-side. Do not rely on the AI agent only passing expected values.
Data disclosure: Make sure your server cannot output data to unauthorized third parties—for example, through unchecked return values.
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