Let’s get excited about ElevenLabs MCP dropped and it’s low-key INSANE!, the new MCP server from ElevenLabs that makes AI integration effortless. No coding is needed to set up voice AI assistants, text-to-speech tools, and AI phone calls.
Let’s walk through a hands-on setup, demos like ordering a pizza and automating customer service calls, and highlight timestamps for Get Started, MCP features, Cursor setup, live chat, and use-cases. Join us in the Voice AI community and follow the video by Jannis Moore for step-by-step guidance and practical examples.
Overview of ElevenLabs MCP
What MCP stands for and why this release matters
We understand that acronyms can be confusing, and ElevenLabs refers to this package as the “MCP server.” While ElevenLabs has used the MCP label to describe this orchestration and runtime layer, they haven’t universally published a single, fixed expansion for the letters. For our purposes, we think of MCP as a modular control plane for model, media, and agent workflows — a centralized server that manages voice models, streaming, and integrations. This release matters because it brings those management capabilities into a single, easy-to-deploy server that dramatically lowers the barrier for building voice AI experiences.
High-level goals: simplify AI voice integrations without coding
Our read of the MCP release is that its primary goal is to simplify voice AI adoption. Instead of forcing teams to wire together APIs, streaming layers, telephony, and orchestration logic, MCP packages those components so we can configure agents and voice flows through a GUI or simple configuration files. That means we can move from concept to prototype quickly, without needing to write custom integration code for every use case.
Core components included in the MCP server package
We see the MCP server package as containing a few core building blocks: a runtime that hosts agent workflows, a TTS and voice management layer, streaming and low-latency audio output, a GUI dashboard for no-code setup and monitoring, and telephony connectors to make and receive calls. Together these components give us the tools to create synthetic voices, clone voices from samples, orchestrate multi-step conversations, and bridge those dialogues into phone calls or live web demos.
Target users: developers, no-code makers, businesses, hobbyists
We think this release targets a broad audience. Developers get a plug-and-play server to extend and integrate as needed. No-code makers and product teams can assemble voice agents from the GUI. Businesses can use MCP to prototype customer service automation and outbound workflows. Hobbyists and voice enthusiasts can experiment with TTS, voice cloning, and telephony scenarios without deep infrastructure knowledge. The package is intended to be approachable for all of these groups.
How this release fits into ElevenLabs’ product ecosystem
In our perspective, MCP sits alongside ElevenLabs’ core TTS and voice model offerings as an orchestration and deployment layer. Where the standard ElevenLabs APIs offer model access and voice synthesis, MCP packages those capabilities into a server optimized for running agents, streaming low-latency audio, and handling real-world integrations like telephony and GUI management. It therefore acts as a practical bridge between experimentation and production-grade voice automation.
Key Features Highlight
Plug-and-play server for AI voice and agent workflows
We appreciate that MCP is designed to be plug-and-play. Out of the box, it provides runtime components for hosting voice agents and sequencing model calls. That means we can define an agent’s behavior, connect voice resources, and run workflows without composing middleware or building a custom backend from scratch.
No-code setup options and GUI management
We like that a visual dashboard is included. The GUI lets us create agents, configure voices, set up call flows, and monitor activity with point-and-click ease. For teams without engineering bandwidth, the no-code pathway is invaluable for quickly iterating on conversational designs.
Text-to-speech (TTS), voice cloning, and synthetic voices
MCP bundles TTS engines and voice management, enabling generation of natural-sounding speech and the ability to clone voices from sample audio. We can create default synthetic voices or upload recordings to produce personalized voice models for assistants or branded experiences.
Real-time streaming and low-latency audio output
Real-time interaction is critical for natural conversations, and MCP emphasizes streaming and low-latency audio. We find that the server routes audio as it is generated, enabling near-immediate playback in web demos, call bridges, or live chat pairings. That reduces perceived lag and improves the user experience.
Built-in telephony/phone-call capabilities and call flows
One of MCP’s standout features for us is the built-in telephony support. The server includes connectors and flow primitives to create outbound calls, handle inbound calls, and map dialog steps into IVR-style interactions. That turns text-based agent logic into live audio sessions with real people over the phone.
System Requirements and Preliminaries
Supported operating systems and recommended hardware specs
From our perspective, MCP is generally built to run on mainstream server OSs — Linux is the common choice, with macOS and Windows support for local testing depending on packaging. For hardware, we recommend a multi-core CPU, 16+ GB of RAM for small deployments, and 32+ GB or GPU acceleration for larger voice models or lower latency. If we plan to host multiple concurrent streams or large cloned models, beefier machines or cloud instances will help.
Network, firewall, and port considerations for server access
We must open the necessary ports for the MCP dashboard and streaming endpoints. Typical considerations include HTTP/HTTPS ports for the GUI, WebSocket ports for real-time audio streaming, and SIP or TCP/UDP ports if the telephony connector requires them. We need to ensure firewalls and NAT are configured so external services and clients can reach the server, and that we protect administrative endpoints behind authentication.
Required accounts, API keys, and permission scopes
We will need valid ElevenLabs credentials and any API keys the MCP server requires to call voice models. If we integrate telephony providers, we’ll also need accounts and credentials for those services. It’s important that API keys are scoped minimally (least privilege) and stored in recommended secrets stores or environment variables rather than hard-coded.
Recommended browser and client software for the GUI
We recommend modern Chromium-based browsers or recent versions of Firefox for the dashboard because they support WebSockets and modern audio APIs well. On the client side, WebRTC-capable browsers or WebSocket-compatible tools are ideal for low-latency demos. For telephony, standard SIP clients or provider dashboards can be used to monitor call flows.
Storage and memory considerations for large voice models
Voice models and cloned-sample storage can grow quickly, especially if we store multiple versions at high bitrate. We advise provisioning ample SSD storage and monitoring disk IO. For in-memory model execution, larger RAM or GPU VRAM reduces swapping and improves performance. We should plan storage and memory around expected concurrent users and retained voice artifacts.
No-code MCP Setup Walkthrough
Downloading the MCP server bundle and unpacking files
We start by obtaining the MCP server bundle from the official release channel and unpacking it to a server directory. The bundle typically contains a run script, configuration templates, model manifests, and a dashboard frontend. We extract the files and review included README and configuration examples to understand default ports and environment variables.
Using the web dashboard to configure your first agent
Once the server is running, we connect to the dashboard with a supported browser and use the no-code interface to create an agent. The GUI usually lets us define steps, intent triggers, and output channels (speech, text, or telephony). We drag and drop nodes or fill form fields to set up a simple welcome flow and response phrases.
Setting up credentials and connecting ElevenLabs services
We then add our ElevenLabs API key or service token to the server configuration through the dashboard or environment variables. The server needs those credentials to synthesize speech and access cloning endpoints. We verify the credentials by executing a test synthesis from the dashboard and checking for valid audio output.
Creating a first voice assistant without touching code
With credentials in place, we create a basic voice assistant via the GUI: define a greeting, choose a voice from the library, and add sample responses. We configure dialog transitions for common intents like “order” or “help” and link each response to TTS output. This whole process can be done without touching code, leveraging the dashboard’s flow builder.
Verifying the server is running and testing with a sample prompt
Finally, we test the setup by sending a sample text prompt or initiating a demo call within the dashboard. We monitor logs to confirm that the server processed the request, invoked the TTS engine, and streamed audio back to the client. If audio plays correctly, our initial setup is verified and ready for more complex flows.
Cursor MCP Integration and Workflow
Why Cursor is mentioned and common integration patterns
Cursor is often mentioned because it’s a tool for building, visualizing, and orchestrating agent workflows and notebooks, and it pairs naturally with MCP’s runtime. We commonly see Cursor used as the design and orchestration layer to create scripts, chain steps, and test logic that MCP then runs in production.
Connecting Cursor to MCP for enhanced agent orchestration
We connect Cursor to MCP by configuring Cursor to call MCP endpoints or by exporting workflows from Cursor into MCP-compatible manifests. This allows us to design multi-step agents in Cursor’s interface and then push them to the MCP server to handle live execution and audio streaming.
Data flow: text input, model processing, and audio output
Our typical data flow is: user text input or speech arrives at MCP, MCP forwards the text to the configured language model or agent logic (possibly via Cursor orchestration), the model returns a text response, and MCP converts that text to audio with its TTS engine. The resulting audio is then streamed to the client or bridged into a call.
Examples of using Cursor to manage multi-step conversations
We often use Cursor to split complex tasks into discrete steps: validate user intent, query external APIs, synthesize a decision, and choose a TTS voice. For example, an ordering flow can have separate nodes for gathering order details, checking inventory, confirming price, and sending a final synthesized confirmation. Cursor helps us visualize and iterate on those steps before deploying them to MCP.
Troubleshooting common Cursor-MCP connection issues
When we troubleshoot, common issues include mismatched endpoint URLs, token misconfigurations, CORS or firewall blockages, and version incompatibilities between Cursor manifests and MCP runtime. Logs on both sides help identify where requests fail. Ensuring time synchronization, correct TLS certificates, and correct content types usually resolves most connectivity problems.
Building Voice AI Assistants
Designing conversational intents and persona for the assistant
We believe that good assistants start with clear intent design and persona. We define primary intents (e.g., order, support, FAQ) and craft a persona that matches brand tone — friendly, concise, or formal. Persona guides voice choices, phrasing, and fallback behavior so the assistant feels consistent.
Mapping user journeys and fallback strategies
We map user journeys for common scenarios and identify failure points. For each step, we design fallback strategies: graceful re-prompts, escalation to human support, or capturing contact info for callbacks. Clear fallbacks improve user trust and reduce frustration.
Configuring voice, tone, and speech parameters in MCP
Within MCP, we configure voice parameters like pitch, speaking rate, emphasis, and pauses. We choose a voice that suits the persona and adjust synthesis settings to match the context (e.g., faster confirmations, calmer support responses). These parameters let us fine-tune how the assistant sounds in real interactions.
Testing interactions: simulated users and real-time demos
We validate designs with simulated users and live demos. Simulators help run load and edge-case tests, while real-time demos reveal latency and naturalness issues. We iterate on dialog flows and voice parameters based on these tests.
Iterating voice behavior based on user feedback and logs
We iteratively improve voice behavior by analyzing transcripts, user feedback, and server logs. By examining failure patterns and dropout points, we refine prompts, adjust TTS prosody, and change fallback wording. Continuous feedback loops let us make the assistant more helpful over time.
Text-to-Speech and Voice Cloning Capabilities
Available voices and how to choose the right one
We typically get a palette of synthetic voices across genders, accents, and styles. To choose the right one, we match the voice to our brand persona and target audience. For customer-facing support, clarity and warmth matter; for notifications, brevity and neutrality might be better. We audition voices in real dialog contexts to pick the best fit.
Uploading and managing voice samples for cloning
MCP usually provides a way to upload recorded samples for cloning. We prepare high-quality, consented audio samples with consistent recording conditions. Once uploaded, the server processes and stores cloned models that we can assign to agents. We manage clones carefully to avoid proliferation and to monitor quality.
Quality trade-offs: naturalness vs. model size and latency
We recognize trade-offs between naturalness, model size, and latency. Larger models and higher-fidelity clones sound more natural but need more compute and can increase latency. For real-time calls, we often prefer mid-sized models optimized for streaming. For on-demand high-quality content, we can use larger models and accept longer render times.
Ethical and consent considerations when cloning voices
We are mindful of ethics. We only clone voices with clear, documented consent from the speaker and adhere to legal and privacy requirements. We keep transparent records of permissions and use cases, and we avoid creating synthetic speech that impersonates someone without explicit authorization.
Practical tips to improve generated speech quality
To improve quality, we use clean recordings with minimal background noise, consistent microphone positioning, and diverse sample content (different phonemes and emotional ranges). We tweak prosody parameters, use short SSML hints if available, and prefer sample rates and codecs that preserve clarity.
Making Phone Calls with AI
Overview of telephony features and supported providers
MCP’s telephony features let us create outbound and inbound call flows by integrating with common providers like SIP services and cloud telephony platforms. The server offers connectors and call primitives that manage dialing, bridging audio streams, and handling DTMF or IVR inputs.
Setting up outbound call flows and IVR scripts
We set up outbound call flows by defining dialing rules, message sequences, and IVR trees in the dashboard. IVR scripts can route callers, collect inputs, and trigger model-generated responses. We test flows extensively to ensure prompts are clear and timeouts are reasonable.
Bridging text-based agent responses to live audio calls
When bridging to calls, MCP converts the agent’s text responses to audio in real time and streams that into the call leg. We can also capture caller audio, transcribe it, and feed transcriptions to the agent for a conversational loop, enabling dynamic, contextual responses during live calls.
Use-case example: ordering a pizza using an AI phone call
We can illustrate with a pizza-ordering flow: the server calls a user, greets them, asks for order details, confirms the selection, checks inventory via an API, and sends a final confirmation message. The entire sequence is managed by MCP, which handles TTS, ASR/transcription, dialog state, and external API calls for pricing and availability.
Handling call recording, transcripts, and regulatory compliance
We treat call recording and transcripts as sensitive data. We configure storage retention, encryption, and access controls. We also follow regulatory rules for call recording consent and data protection, and we implement opt-in/opt-out prompts where required by law.
Live Chat and Real-time Examples
Demonstrating a live chat example step-by-step
In a live chat demo, we show a user sending text messages to the agent in a web UI, MCP processes the messages, and then it either returns text or synthesizes audio for playback. Step-by-step, we create the agent, start a session, send a prompt, and demonstrate the immediate TTS output paired with the chat transcript.
How live text chat pairs with TTS for multimodal experiences
We pair text chat and TTS to create multimodal experiences. Users can read a transcript while hearing audio, or choose one mode. This helps accessibility and suits different contexts — some users prefer to read while others want audio playback.
Latency considerations and optimizing for conversational speed
To optimize speed, we use streaming TTS, pre-fetch likely responses, and keep model calls compact. We monitor network conditions and scale the server horizontally if necessary. Reducing round trips and choosing lower-latency models for interactive use are key optimizations.
Capturing and replaying sessions for debugging
We capture session logs, transcripts, and audio traces to replay interactions for debugging. Replays help us identify misrecognized inputs, timing issues, and unexpected model outputs, and they are essential for improving agent performance.
Showcasing sample interactions used in the video
We can recreate the video’s sample interactions — a pizza order, a customer service script, and a demo call — by using the same agent flow structure: greeting, slot filling, API checks, confirmation, and closure. These samples are a good starting point for our own custom flows.
Conclusion
Why the MCP release is a notable step for voice AI adoption
We see MCP as a notable step because it lowers the barrier to building integrated voice applications. By packaging orchestration, TTS, streaming, and telephony into a single server with no-code options, MCP enables teams to move faster from idea to demo and to production.
Key takeaways for getting started quickly and safely
Our key takeaways are: prepare credentials and hardware, use the GUI for rapid prototyping, start with mid-sized models for performance, and test heavily with simulated and real users. Also, secure API keys and protect administrative access from day one.
Opportunities unlocked: no-code voice automation and telephony
MCP unlocks opportunities in automated customer service, outbound workflows, voice-enabled apps, and creative voice experiences. No-code builders can now compose sophisticated dialogs and connect them to phone channels without deep engineering work.
Risks and responsibilities: ethics, privacy, and compliance
We must accept the responsibilities that come with power: obtain consent for voice cloning, follow recording and privacy regulations, secure sensitive data, and avoid deceptive uses. Ethical considerations should guide deployment choices.
Next steps: try the demo, join the community, and iterate
Our next steps are to try a demo, experiment with voice clones and dialog flows, and share learnings with the community so we can iterate responsibly. By testing, refining, and monitoring, we can harness MCP to build helpful, safe, and engaging voice AI experiences.
If you want to implement Chat and Voice Agents into your business to reduce missed calls, book more appointments, save time, and make more revenue, book a discovery call here: https://brand.eliteaienterprises.com/widget/bookings/elite-ai-30-min-demo-call
