Bridging AI Islands

MCP Meets OVON in the Quest for True Interoperability

Mar 10, 2025

Imagine you're chatting with your AI assistant about last quarter's sales figures. Instead of vague generalities, your assistant instantly reaches into your company's live database via a standard interface, fetches the exact numbers, and responds with pinpoint accuracy. Moments later, you pivot the conversation to booking travel, and seamlessly, a specialized travel assistant takes over, smoothly picking up the thread without missing a beat.

Sound futuristic? Perhaps. But two open standards are actively building the foundation for this future: Model Context Protocol (MCP) and Open Voice Network (OVON).

MCP: Breaking the Data Barrier for AI

Introduced by Anthropic in late 2024, MCP addresses a core limitation of AI—its isolation from real-time, external data sources. Large language models (LLMs) typically rely on static training data, making them less effective for dynamic queries. MCP changes this by standardizing AI-to-data interactions, functioning like a universal "USB-C for AI"—a standardized, plug-and-play protocol.

In technical terms, MCP uses a client-server architecture. An AI application acts as the MCP client, connecting via JSON-based messages to MCP servers—each dedicated to specific external tools or data sources such as Slack, GitHub, or SQL databases. MCP servers handle both data retrieval (e.g., fetching documents, database queries) and executing actions (sending emails, updating records). Anthropic’s Claude Desktop demonstrates this capability by directly querying user files or repositories mid-conversation.

While much of the current discourse centers on MCP's Tools concept—enabling AI models to perform actions through server-exposed functionalities—it's essential to recognize that MCP also encompasses several other foundational concepts, many of which are yet to be widely implemented:

Resources: This concept allows servers to expose data and content that clients can read and use as context for LLM interactions. Resources can include file contents, database records, API responses, and more.
Prompts: Servers can define reusable prompt templates and workflows, facilitating standardized and shareable LLM interactions. These prompts can accept dynamic arguments, include context from resources, and guide specific workflows.
Sampling: This feature enables servers to request LLM completions through the client, supporting sophisticated agentic behaviors while maintaining security and privacy. The sampling process involves the server sending a request to the client, which then samples from an LLM and returns the result.
Roots: Roots define the boundaries within which servers operate, providing a way for clients to inform servers about relevant resources and their locations. They serve to guide servers, clarify resource boundaries, and organize multiple resources simultaneously.
Transports: Transports handle the underlying mechanics of how messages are sent and received between clients and servers. MCP includes standard transport implementations like Standard Input/Output (stdio) and Server-Sent Events (SSE), and it allows for custom transport implementations to meet specific needs.

As MCP continues to evolve, the broader adoption and implementation of these concepts will be crucial in realizing its full potential for AI interoperability and functionality.

MCP's strength lies in its standardized API, allowing consistent integration across multiple languages (with SDKs in Python, TypeScript, Java), significantly accelerating development cycles. Yet, MCP primarily addresses single-agent use cases—one AI agent extending its context and capabilities. But what about scenarios involving multiple AI agents coordinating seamlessly?

OVON: Enabling Assistant-to-Assistant Collaboration

That's precisely where OVON comes into play. While MCP solves the AI-to-external-world problem, OVON solves the AI-to-AI interoperability challenge. Developed under the Linux Foundation's Open Voice Network, OVON provides a standard conversational framework enabling multiple AI assistants to interact smoothly in real-time, irrespective of their underlying technology or vendor.

OVON's central innovation is the Conversation Envelope, a structured JSON-based message format encapsulating dialogue content, metadata (speaker ID, timestamps), and dialog control commands (Dialogue Events). These Dialogue Events include:

Utterance: The core message content.
Invite: Bringing another agent into the conversation.
Bye: An agent leaving the dialogue.
Whisper: Private agent-to-agent messages used for internal coordination without disrupting the user experience.

OVON's architecture involves a "conversation floor manager," coordinating conversation flow, ensuring clarity, and managing dialogue turns among agents. Each participating assistant also provides an "Assistant Manifest," describing its capabilities and interfaces, facilitating dynamic agent discovery and selection.

An early example of OVON in action is Estonia's government-backed Bürokratt project. Citizens interact with multiple specialized AI agents handling public services (health, transportation, taxes) through a unified, OVON-based voice interface. This interoperability is akin to browsing websites through a standardized HTTP protocol, but for AI conversations.

MCP and OVON: A Complementary Ecosystem

MCP and OVON aren't competing; rather, they're highly complementary. Imagine an OVON-enabled conversational system where a user’s question is dynamically routed from a general assistant to a specialized financial assistant. That financial agent, equipped with MCP, queries real-time databases for accurate insights, returning precise information seamlessly back through OVON's conversation envelope. The user experiences no disruption, enjoying a seamless conversational flow.

In this combined approach, OVON manages inter-agent dialogue (which assistant handles a task and when), while MCP empowers individual assistants with external data and tool integration.

Challenges and Forward-Looking Perspectives

Both standards, while promising, face early-stage adoption hurdles. MCP requires broad industry acceptance to realize its full potential, whereas OVON's challenge lies in securing buy-in from existing voice-assistant leaders. Technical complexities also exist: MCP deployments necessitate managing numerous MCP server integrations, and OVON implementations must carefully handle context sharing, conversation continuity, and latency in real-time dialogue handoffs.

Nevertheless, both MCP (with early adopters like Anthropic, Sourcegraph, Replit) and OVON (backed by the Linux Foundation, with public-sector projects like Bürokratt) demonstrate strong momentum toward widespread AI interoperability.

The Road Ahead

The future isn't isolated AI islands but interconnected archipelagos, powered by standards like MCP and OVON. As adoption grows, developers and enterprises could soon experience intelligent assistants seamlessly accessing real-time knowledge and cooperating dynamically. Ultimately, MCP and OVON promise a future where AI interactions are not just smarter, but fundamentally more collaborative and contextually aware.