Convolo AI

@convolo-ai list all available voice types and recommend one that sounds professional but approachable for a customer support character

Based on the available voices, I recommend 'en-US-Neural2-J' for your customer support character. This voice offers a warm, professional tone that balances authority with approachability. Other options include 'en-US-Neural2-F' (slightly more formal) and 'en-US-Neural2-C' (younger, energetic). The neural voices provide natural intonation and pacing that work well for extended customer interactions. All voices support real-time streaming for low-latency responses.

This example demonstrates the MCP's voice discovery capability, which is typically the first step in character creation. Voice availability depends on your Convolo AI plan tier. The MCP returns voice IDs that you'll reference when creating characters, so save the recommended ID for the next step.

@convolo-ai create a new customer support character named 'Alex' with the en-US-Neural2-J voice, backstory as a helpful product specialist with 5 years experience, and actions for checking order status and processing returns

Character 'Alex' created successfully (ID: char_8k3mq9). Configured with en-US-Neural2-J voice and backstory emphasizing product expertise. Two actions defined: 'check_order_status' (requires order_id parameter) and 'process_return' (requires order_id and reason). Alex is now ready for conversations but requires core AI settings (temperature, max tokens) before deployment. The character will reference these actions when customers mention orders or returns in conversation.

This write operation creates a persistent character in your Convolo AI workspace. The returned character ID is required for all subsequent updates and conversations. Actions defined here determine what the character can actually do beyond conversation—you'll need to connect these to your backend systems separately. Character creation counts against your account's character limit.

@convolo-ai evaluate the performance of character char_8k3mq9 based on the last 50 customer interactions, focusing on response accuracy and action completion rate

Performance evaluation for 'Alex' (char_8k3mq9) across 50 interactions: Response accuracy 87% (43/50 queries answered correctly on first attempt). Action completion rate 92% (35/38 action requests successfully executed). Common issues: 3 instances of misinterpreting order numbers with similar digits, 2 timeout errors on return processing. Positive signals: Average customer satisfaction score 4.2/5, 78% of conversations resolved without escalation. Recommendation: Add clarification prompts for order number confirmation and increase timeout threshold for return processing actions.

This synthesis example shows how the MCP combines conversation data with AI analysis to surface actionable insights. Evaluation requires you to first fetch conversation sessions, so this is a multi-step workflow. The metrics returned depend on what interaction data you've logged—if you're not tracking satisfaction scores or action outcomes, the evaluation will be limited to conversational patterns only.

When Convolo AI fits early-stage product onboarding flows

A 6-person SaaS startup wants a conversational onboarding assistant that walks new users through account setup and feature discovery. Convolo AI is a strong fit here because the Create Character and Set Core AI Settings tools let you script a personality-driven guide without hiring voice actors or building custom NLP. The Get Character Response tool handles both text and audio input, so you can test the flow in Slack before deploying it in-app. The threshold: if your onboarding needs to pull live account data or trigger backend actions (like provisioning a workspace), you'll hit Convolo's limits fast—the Define Character Actions tool is more about dialogue branching than API orchestration. If your flow is mostly educational and under 10 decision points, this MCP saves you two weeks of chatbot plumbing.

Why Convolo AI struggles with high-volume support routing

A 15-person e-commerce team wants an AI character to handle first-contact support questions (order status, return policy, shipping delays) before escalating to human agents. Convolo AI's voice and personality tools sound appealing, but the MCP doesn't expose integrations with ticketing systems or order databases—you're limited to the 10 tools here, none of which query external CRMs or fulfillment platforms. The Evaluate Character Performance tool helps you tune responses after the fact, but without real-time context from Shopify or Zendesk, your character will give generic answers that frustrate customers. If your support volume is under 50 tickets a week and you're okay with a purely conversational (non-transactional) bot, Convolo works. Beyond that, you need a CRM-native MCP with actual data connectors.

When Convolo AI accelerates sales coaching at small scale

A 4-person sales team wants reps to practice objection handling and discovery calls against an AI prospect before live demos. Convolo AI is ideal for this: the Create Character tool lets you build personas (skeptical CFO, budget-conscious ops manager) with custom backstories, and the Generate Starter Conversation tool seeds realistic opening lines. The Get Character Response tool delivers voice replies, so reps can rehearse over Zoom or in Switchy's voice channels. The limit: if you need to score calls against a rubric or integrate with your CRM to pull real prospect data, Convolo's Evaluate Character Performance tool is too lightweight—it's qualitative feedback, not structured analytics. For teams under 10 reps running 5-10 role-plays a week, this MCP cuts your coaching prep time in half.