MCP and the need for AI interoperability: Why we must avoid vendor lock-in to ensure a unified future
Daniel Levi
Posted On March 8, 2025
Artificial intelligence is advancing at a neck-breaking speed, but a critical issue looms: AI models, especially large language models (LLMs), need real-time data from external sources to deliver relevant, context-aware responses.
Today, developers must build custom integrations for every API—whether it’s flight prices from airlines or updates from Slack. This process is slow, costly, and a maintenance nightmare. Without a standardized way to connect AI to the world, scalability falters, and innovation slows. The industry needs a universal solution, but the path forward is fraught with challenges.
To address this, Anthropic introduced the Model Context Protocol (MCP), an open-source standard launched on November 24, 2024. MCP is designed to streamline how AI assistants, particularly LLMs, connect to external data sources in a seamless and standardized way.
By eliminating the complexity of managing multiple APIs, MCP provides AI systems with a universal method to access real-time data without extensive custom integrations
“Model Context Protocol (MCP) is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses,” Anthropic said in a blog post.
Model Context Protocol (MCP): A Step Toward AI Interoperability
Currently, AI models struggle with integrating data from diverse sources. For example, if an AI assistant needs to check flight prices across multiple airlines, it must connect to each airline’s API separately—each with its own format, rate limits, and rules. While manageable for a handful of integrations, this approach becomes unscalable when dealing with hundreds of sources.
MCP solves this problem by acting as a universal adapter, much like USB-C for hardware. Instead of building individual connections for every new data source, AI applications integrate once with MCP, which then standardizes and delivers data from various sources. This simplifies AI development, reduces overhead, and enhances interoperability across different platforms.
Credit: Raygun.com
How MCP Works
MCP operates on a client-server model, creating a standardized method for AI models to access external data:
- MCP Servers: These expose data or tools from sources like Google Drive, Slack, GitHub, or Postgres.
- MCP Clients: AI applications that connect to MCP servers to retrieve relevant information.
By eliminating the need for custom-built integrations, MCP reduces development complexity and makes AI models more scalable. Developers no longer need to reinvent the wheel for each integration—MCP handles the heavy lifting.
To accelerate adoption, Anthropic has open-sourced MCP, providing SDKs in Python and TypeScript, along with pre-built servers for major platforms. Currently, MCP supports only local server setups, but enterprise-grade remote support is in development.
Who Shoulders The Burden of AI Agent Integration With MCP?
A major shift MCP introduces is who does the heavy lifting—the companies providing data or the developers building AI models?
- With MCP: The burden is on data providers (airlines, stock markets, weather services). They have to structure their data according to MCP’s format, making it easier for AI systems to retrieve information.
- Without MCP: The workload falls on developers, who must integrate and maintain multiple APIs separately, leading to inefficiencies and scaling issues.
If MCP were an open standard, this shift would be great for developers. However, if one company owns the protocol, businesses might hesitate to participate, fearing reliance on a single entity that can change the rules at any time.
Why Hasn’t OpenAI Built Its Own MCP?
If a universal standard for AI interoperability is so important, why hasn’t OpenAI taken the lead on this? It comes down to strategy and control.
- Its Business Model Relies on APIs. OpenAI makes money by selling access to AI models through APIs. If a universal protocol like MCP took off and became an open standard, it would reduce the need for direct API integrations, potentially cutting into OpenAI’s revenue.
- OpenAI Wants to Control How AI Interacts with Data. OpenAI benefits from keeping direct control over how AI retrieves information. If a single protocol became the default standard for AI data exchange, OpenAI could lose some of that control, which might not align with its long-term strategy.
- Waiting to See What Happens. OpenAI and other big players may be watching MCP’s adoption before making a move. If MCP gains traction, they might build their own version—or push for a competing standard that keeps them in control.
How MCP Compares to OpenAI’s Current Approach
Here’s how MCP’s publisher-centric model stacks up against OpenAI’s developer-centric model:
| Feature | MCP (Publisher-Centric) | OpenAI (Subscriber-Centric) |
|---|---|---|
| Who does the work? | Data providers structure their data | Developers must manually integrate each API |
| Integration Effort | Easier for AI models | Harder, as every API requires custom integration |
| Scalability | AI models can scale without extra API work | Each new integration adds more complexity |
| Control | Shared among participants | Controlled by OpenAI or the API provider |
| Real-time Data Access | Seamless via MCP standardization | Requires ongoing API maintenance |
MCP would make AI development easier if it were truly open. But if Anthropic holds the keys, other AI companies and developers might be forced to play by their rules.
MVP and The Risk of Vendor Lock-In
Despite its benefits, MCP raises serious concerns about vendor lock-in. While Anthropic has released it as an open-source standard under the MIT license, the reality is that MCP is Anthropic’s brainchild—not a neutral standard from an international body like W3C or ISO. This means that Anthropic remains the sole steward of its evolution, potentially shaping its development in ways that favor its own AI models, like Claude, over competitors.
The Hidden Trade-Off: From Fragmentation to a Single Gatekeeper
MCP promises to solve AI interoperability by streamlining access to external data, but if it becomes the default while remaining under Anthropic’s control, it won’t truly fix the problem—it will just shift control from fragmented APIs to a single gatekeeper.
Here’s what that means in practice:
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One Company Can Dictate Access: If Anthropic owns MCP, it can decide who gets access and on what terms. They could introduce paywalls, restrict competitors, or favor their own AI models, making it harder for independent AI developers to compete.
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Innovation Slows Down: AI companies would be forced to comply with a single company’s standards, limiting their ability to experiment, customize, or innovate outside of those predefined rules. Instead of fostering an open AI ecosystem, MCP could consolidate power in the hands of one company, slowing progress across the industry.
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The Market Splits: If MCP becomes too Anthropic-centric, other major players like OpenAI, Google, and Microsoft might respond by creating their own competing protocols. This would put the industry back to square one, with fragmented AI ecosystems instead of true interoperability. Instead of solving the problem, MCP could unintentionally accelerate the AI protocol wars, leading to competing standards and closed ecosystems.
Historical Precedent: The Pitfalls of Proprietary Control
Tech history warns against proprietary control disguised as openness:
- HTML’s Early Promise → Originally envisioned as a universal standard, HTML led to browser wars when companies introduced proprietary extensions, fragmenting the web.
- Mobile Ecosystems → Apple’s iOS and Google’s Android started as platforms for developers but evolved into walled gardens, making it nearly impossible to switch ecosystems without high costs.
MCP risks following the same path. If developers build their integrations around Anthropic’s vision, they may later find themselves locked in—dependent on its updates, pricing, and policies, with no viable alternative.
AI’s High Stakes: A Future Controlled by a Few?
Unlike browsers or mobile platforms, AI isn’t just another software category—it’s the backbone of future industries. Whoever controls how AI accesses and processes information will shape entire markets, from finance and healthcare to cybersecurity and education.
If MCP becomes the dominant standard but remains under Anthropic’s control, it could:
- Centralize AI’s evolution in a handful of companies
- Limit competition by favoring proprietary models
- Stifle innovation by enforcing rigid standards
While MCP’s open-source nature allows for contributions and forks, the bigger question is: Will the AI industry rally behind it as a truly open standard, or will it remain an Anthropic-dominated protocol that ultimately re-centralizes AI interoperability under a single entity?
Why AI Interoperability Is Non-Negotiable
For AI to scale responsibly, a common language is essential. Without interoperability, AI development remains fragmented, leading to inefficiencies, inconsistencies, and limited innovation.
A Unified Future
Today, every major AI model requires custom integrations to access external data. Developers must manage different APIs, formats, and permissions, creating unnecessary complexity. This slows down AI adoption, increases costs, and leads to inconsistent user experiences. A universal standard like MCP could solve this, but only if it remains truly open and neutral.
Avoiding the Walled Gardens
Tech companies have a long history of turning open platforms into closed ecosystems. Apple’s iOS and Google’s Android started as enablers but evolved into heavily controlled environments where they dictate terms to developers. If MCP remains under Anthropic’s control, it risks becoming a similar system—initially open, but ultimately designed to benefit one company’s ecosystem over the broader industry.
The User Perspective
End users—businesses, researchers, and individuals—should not have to worry about vendor lock-in when using AI tools. Interoperability benefits everyone by ensuring AI systems work seamlessly across platforms rather than trapping users within a single vendor’s ecosystem. If AI is to democratize access to intelligence, it must be built on neutral, universally accepted protocols rather than proprietary systems disguised as open standards.
Beyond MCP: The Case for Neutral Governance
For AI interoperability to succeed, neutral governance is essential. If one company controls the standard, it can dictate access, influence competition, and shape AI’s future in ways that serve its own interests rather than the broader industry’s.
Who Owns the Standard?
Currently, MCP is an Anthropic-driven initiative, not an industry-wide standard backed by an independent body like the IETF or ISO. This raises concerns about long-term neutrality. Without a shift toward a multi-stakeholder governance model, MCP could become an industry bottleneck rather than a true enabler of AI interoperability.
Community vs. Control
Open-source adoption does not automatically translate to shared governance. If MCP gains widespread use, key questions remain:
- What happens if Anthropic decides to pivot its direction?
- Can competitors contribute meaningfully, or will they always be at a disadvantage?
- How will independent organizations, governments, and enterprises ensure that MCP remains fair and unbiased?
For MCP to be more than a proprietary standard in disguise, its roadmap must be shaped by an independent body representing the broader AI ecosystem. Otherwise, it risks becoming another closed system that limits innovation and competition.
A Call to Action
There are two possible paths forward:
- Anthropic hands over MCP’s governance to a neutral AI standards organization, ensuring it remains an open and fair protocol.
- The industry creates an independent alternative, developing a truly open AI interoperability standard that is not controlled by a single company.
Without one of these solutions, MCP may simply shift the AI interoperability problem from fragmented APIs to a single dominant gatekeeper.
The Stakes: A Unified AI Future
AI’s future depends on whether interoperability is built on open, inclusive standards or controlled by a small group of companies.
What’s at Risk
If MCP becomes the dominant standard but remains under Anthropic’s control, the AI ecosystem could face serious challenges:
- A fragmented AI landscape, where systems are incompatible, forcing businesses to choose between closed ecosystems.
- A monopolized AI market, where a handful of companies dictate how AI evolves, limiting access for smaller players.
- An AI future shaped by corporate interests rather than by independent research, public input, or ethical considerations.
The Opportunity
If AI interoperability is handled correctly, it could level the playing field for businesses, developers, and researchers. A truly open standard would:
- Allow AI systems to communicate seamlessly, much like the internet operates today.
- Enable developers to build without fear of vendor lock-in.
- Create a universal protocol for AI that is as essential and foundational as TCP/IP for the internet.
Conclusion
MCP is a step in the right direction, but it is not enough. AI interoperability must be governed by neutral, independent bodies to prevent vendor lock-in and ensure that innovation remains open to all. Without industry-wide oversight, AI could become yet another space dominated by a few powerful players, restricting access, competition, and progress.
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