Anthropic's Model Context Protocol (MCP): I Am Not Convinced Yet

Model Context Protocol (MCP) – Scalability, Feasibility, and Market Potential

The Model Context Protocol (MCP) is a new open standard (open-sourced by Anthropic in late 2024) for connecting AI models to the external data sources and tools they need. Instead of building custom connectors for each data source or API, MCP defines a universal client–server protocol that AI assistants (clients) use to interact with any MCP-compliant data/tool server. In essence, “think of MCP like a USB-C port for AI applications” a standardized plug-and-play interface between AI and diverse systems. This evaluation examines MCP from both technical and strategic angles, comparing it to simpler agent connectors, traditional API integrations, and AI toolchain frameworks.

1. Technical Feasibility & Scalability

Architecture for Multiple Data Sources

MCP is explicitly designed to handle M clients and N data sources/tools without requiring M×N custom integrations. By standardizing interactions, it transforms this into an M+N problem. An AI application only needs to implement MCP once to gain access to any number of MCP-compatible tools. Each data source is exposed via a lightweight MCP server, and an AI client can connect to many such servers simultaneously. This modular client-server setup means new tools can be added or removed independently, supporting horizontal scalability as needs grow. Early implementations (e.g. Claude Desktop, IDE plugins) have demonstrated connecting to multiple sources like Google Drive, GitHub, Slack, etc., in one unified workflow.

Performance & Latency

MCP uses efficient communication mechanisms (JSON-RPC 2.0 over stdin/stdout for local plugins or HTTP with Server-Sent Events for remote). This keeps message overhead minimal – comparable to calling a local function or a REST API. In practice, the latency added by MCP is modest (a JSON serialization and possibly an HTTP call) and often dwarfed by the LLM’s own processing time. Moreover, by providing direct, on-demand access to relevant data, MCP can improve overall responsiveness and accuracy of the AI. Instead of the model making multiple guesses or hallucinating due to lack of information, it can fetch needed facts in one step, leading to faster convergence on correct answers.

There is some overhead in that each tool call consumes a bit of time and context window space (the results must be fed back into the model), so workflows with very frequent tool usage might incur cumulative latency. However, this trade-off is similar for any tool-use approach (OpenAI function calls or LangChain agents also require separate calls per tool). On balance, MCP’s design emphasizes streaming results (via SSE) and concurrency, which are conducive to low latency and scalable performance.

Security and Maintainability

A notable technical strength of MCP is its built-in support for security and governance. Connections are “local-first” where possible, meaning an AI tool can run connectors on the user’s infrastructure, keeping data private. The protocol supports scoped permissions and user-in-the-loop confirmation for actions. For example, an AI model might list available tools and require a user to approve using a particular tool (as Claude’s implementation does). This design reduces the risk of unintended or harmful actions by the AI, a concern with unrestricted tool use. In terms of maintainability, standardization is a clear win: rather than each application duplicating integration code, improvements to an MCP server immediately benefit any AI client using it. Logging and auditing are simplified as well – organizations can monitor all AI tool usage through the MCP layer, rather than chasing logs across countless custom integrations.

One weakness to note is that introducing a new protocol and extra processes does add complexity to system architecture. Developers must run and manage MCP server processes (or libraries) and ensure their AI client knows how to handle the protocol. This could introduce new failure modes or debugging challenges compared to a simple direct API call. However, the MCP spec and provided SDKs (in Python, TypeScript, Java, etc.) aim to make implementation straightforward.

In summary, MCP appears technically feasible and scalable: it builds on proven patterns (JSON-based RPC, client-server plugins) and addresses multi-tool integration cleanly. The main trade-off is the additional abstraction layer, which needs to be justified by sufficient reuse and reliability gains.

2. Adoption & Market Dynamics

Business Case for Industry Adoption

The promise of MCP is to dramatically reduce the integration burden as AI becomes ubiquitous across enterprise and consumer apps. Today, companies often expend significant effort wiring their AI solutions to internal data silos, third-party SaaS APIs, and legacy systems – often writing one-off glue code for each case. MCP offers a one-and-done alternative: “instead of building one-off integrations for every data source, plug into a universal protocol”.

This means faster development of AI features and lower maintenance costs over time. For example, an enterprise could use a standard MCP connector for its Salesforce data, its internal wikis, and its database, rather than maintaining separate API clients for each in their chatbot. The sustainability factor is key – “build once and reuse [connectors] across multiple LLMs and clients—no more rewriting the same integration in a hundred different ways.”. If MCP gains traction, an ecosystem of pre-built connectors (MCP servers) can emerge, similar to how device drivers or database ODBC drivers exist for most systems. This network effect benefits everyone: AI vendors can focus on model quality while third-parties provide connectors, and businesses can mix-and-match knowing everything speaks MCP. Early adopters like Block (Square), Apollo, Replit, Codeium, and Sourcegraph have shown interest, integrating MCP to enhance AI capabilities in their platforms. Such endorsements hint at a real need for standardized AI integration in coding tools and enterprise contexts.

Does MCP Simplify or Overcomplicate?

This question is central to market acceptance. In theory, MCP greatly simplifies the landscape by unifying it – one protocol to learn, one set of connectors to use. Without MCP, developers juggle a patchwork of APIs, SDKs, auth methods, and plugins for each tool. As one analysis put it, before MCP you might deal with “separate plugins, tokens, or custom wrappers” for each data source, whereas with MCP “the LLM can ‘see’ all registered connectors” through one interface. This uniformity is analogous to how adopting USB-C replaced a tangle of proprietary chargers, or how TCP/IP unified networking – it reduces friction in the long run. However, in practice, MCP does introduce an additional layer that may seem unnecessary for simple use cases. For a small-scale application that just needs to call one or two well-documented APIs, using MCP could be over-engineering. In such cases, a direct REST call or a lightweight library might be easier.

The key is whether MCP’s benefits compound as complexity grows. In an enterprise setting with dozens of data integrations, MCP likely simplifies operations; in a hobby project with one tool, it might complicate it. The market will weigh this carefully.

Open-Source Adoption and Ecosystem

Anthropic’s decision to open-source MCP (spec and reference servers on GitHub) indicates a strategic push for broad industry adoption. For MCP to become an industry-wide standard, it must gain trust beyond its originator. A positive sign is that other major AI players are exploring compatibility. For instance, Microsoft’s Semantic Kernel team highlighted MCP’s interoperability and even provided a guide to convert MCP tools into Semantic Kernel functions. This shows that MCP can be embraced by third-party frameworks and is not inherently tied to one vendor’s model. Similarly, tools exist to integrate MCP with LangChain agents (community contributors have created wrappers to use MCP servers as LangChain tools. These efforts ease adoption by bridging MCP into existing development workflows.

Challenges remain, however.

One is the “chicken-and-egg” network effect: developers won’t adopt MCP unless it has rich connectors and broad model support, but creating those connectors and model integrations requires developer effort up front. Anthropic seeded the ground by releasing several MCP servers (for Google Drive, Slack, Git, Postgres, etc.), but community contributions will determine if the catalog grows to cover the long tail of enterprise systems.

Another concern is vendor lock-in perceptions. MCP’s goal is actually the opposite of lock-in – it explicitly aims for “flexibility to switch between LLM providers and vendors” by decoupling data connectors from any single model. But if, say, only Anthropic’s Claude supported MCP natively at first, users might worry it’s a ploy to favor Claude.

The best antidote is more LLMs and platforms supporting MCP (through clients or adapters), so that using MCP truly means you can plug any model. The involvement of open-source projects and companies like Block and Microsoft suggests MCP has a chance to become a neutral standard rather than a proprietary one. Still, industry-wide adoption typically requires some governance, perhaps a consortium or standards body backing – to ensure longevity and fairness. In summary, MCP’s adoption will hinge on demonstrating clear ROI (faster integration, fewer bugs, easier scaling) and alleviating fears that it’s just extra complexity or tied to one vendor.

The business case appears strong in environments with many AI integration points, whereas winning over the broader market will require continued evangelism and proof of its advantages.

3. Comparative Benchmarks

To better understand MCP’s value, it helps to compare it directly with alternative approaches to AI integration:

MCP vs. Simpler Agent-Based Connectors:

Many current AI solutions use ad-hoc agent connectors, essentially, custom code that lets an AI agent perform certain actions (e.g. a script that an LLM can call to query a database or scrape a webpage). In enterprise scenarios, these might be proprietary connectors or RPA bots; in consumer AI (like digital assistants), they’re often hard-coded skills (e.g. a weather skill); in research projects (AutoGPT-style agents), they might be Python functions or tools passed into an LLM. The advantage of these simpler connectors is that they are quick to implement for a specific need and optimized for that context. However, they don’t scale well: each new tool or data source is another bespoke integration, and different AI applications reinvent the wheel each time. This leads to a fragmented ecosystem where “improvements to one integration rarely benefit the broader ecosystem”.

MCP's Strength

MCP’s strength here is standardization and reuse. It provides a formal “directory of capabilities” that any AI agent can discover and use. Think of MCP connectors like device drivers for AI agents: instead of every AI agent having its own way to interface with, say, a calendar or CRM, they all use a common driver. Historically, this kind of standardization has been very powerful. For example, the Open Database Connectivity (ODBC) standard meant applications no longer needed custom code for each database, they could rely on a common interface to any SQL database. MCP aspires to be “a foundational layer for AI integration, much like ODBC was for database connectivity”, simplifying development and making AI tools more interchangeable.

MCP's Weakness

The weakness compared to ad-hoc connectors is initial overhead: setting up an MCP server or client may be overkill if you just want a one-off script. In scenarios where a quick hack suffices, MCP can feel like using a formal API where a simple direct query might do. Nonetheless, for maintainability and scaling to many integrations, MCP clearly outshines scattershot agent connectors.

MCP vs. API-Based Integrations (OpenAPI/REST)

A huge portion of integrations today are built on RESTful APIs with JSON and described by OpenAPI (formerly Swagger) specs. These are mature and widely adopted standards for web services. In fact, OpenAPI plays a role in some AI integration schemes. E.g. OpenAI’s ChatGPT Plugins require an OpenAPI spec for the plugin’s API, and the AI model then reads that to call the API. In a sense, OpenAPI+REST is the “traditional” way to let software (or an AI) access a service. MCP is not trying to replace the underlying APIs; rather, it operates at a higher level of abstraction for the AI’s benefit. Under the hood, an MCP server might call those same REST APIs to fulfill a request. The difference is it presents a uniform interface to the AI model. Instead of each API having distinct endpoints and auth, the AI just sees a list of available tools with standardized schemas and invokes them via tools/call messages.

One way to view MCP is as an adapter

It adapts various APIs (or databases, etc.) into a consistent JSON-RPC-based format that language models can work with easily. For example, rather than manually prompting an LLM with a raw HTTP request to Slack’s API, a developer could use an MCP Slack connector that exposes a “send_message” tool with defined input fields – the LLM doesn’t need to know the HTTP details, just how to call that tool. Another key distinction is bidirectionality and context handling.

REST APIs are usually stateless request/response. MCP, by contrast, is designed to maintain session context with the AI client. It can feed documents or data chunks into the model’s context window (as resources or prompts) and not just accept commands. This two-way flow (hence “Context” Protocol) means an AI can query data, get back information, and incorporate it into its response in a seamless loop. Traditional APIs alone don’t provide that integration into the model’s prompt context. It has to be bolted on by the developer. MCP streamlines this by defining how data is returned (e.g. content payloads annotated for the model). In terms of standards comparison: OpenAPI is a descriptive standard (for humans/clients to know how to call an API) whereas MCP is an execution standard (for AI to actually call and retrieve). They are complementary – indeed, we’re seeing tools that can autogenerate MCP connectors from OpenAPI specs.

That suggests MCP isn’t a competitor to REST/GraphQL/etc., but a layer on top that could unify how AI agents consume those APIs. The market dynamic here will be interesting: if companies start shipping “MCP endpoints” alongside their REST API (as envisioned by some, where turning on an “MCP toggle” in API generators produces an MCP server), it could drastically simplify AI integrations. In summary, API integrations today are ubiquitous and well-understood; MCP leverages them but adds a uniform AI-friendly interface and context management. It’s analogous to how OpenAPI standardized REST descriptions. MCP attempts to standardize how AI uses any API or data source.

MCP vs. AI Toolchain Frameworks (LangChain, Semantic Kernel, etc.):

LangChain, Semantic Kernel, Haystack, and similar frameworks have become popular for building AI applications. They provide modules for things like chaining prompts, memory management, and crucially, tool integrations (often calling them “tools” or “skills”). For example, LangChain allows developers to define a tool (a Python function) that an agent can use, and frameworks like Microsoft’s Semantic Kernel allow plugins or skill functions. These frameworks are developer-centric: they’re essentially libraries in code that help orchestrate an LLM’s behavior. By contrast, MCP is more of a protocol standard to bridge external systems and any AI client. The difference can be seen in scope: “While MCP focuses on standardizing data access for AI systems, LangChain and [Semantic Kernel] offer comprehensive toolkits for building and deploying LLM-powered applications.”.

In practice, this means MCP and these frameworks can complement rather than replace each other. For instance, Semantic Kernel can act as an MCP client – as demonstrated in Microsoft’s guide, one can convert MCP tools into Semantic Kernel functions, then use OpenAI’s function calling to have a GPT model invoke those functions. Similarly, a LangChain agent could use an MCP connector as one of its tools (and indeed adapters exist for this).

The strengths of frameworks like LangChain are their rich features for building logic (planning multi-step reasoning, integrating with vector databases, etc.) and their large community of ready-made tool implementations. However, those tool implementations are not standardized. A LangChain “Google Search API tool” is written for LangChain specifically, and another framework can’t directly use it without re-writing. MCP’s advantage is in providing a neutral ground: any tool written as an MCP server can be used by any compliant client, whether that client is a Claude AI app, a custom Python script, or a LangChain agent with the right wrapper. It’s akin to the difference between a single application’s plugin system versus a cross-application standard.

A weakness of MCP relative to these frameworks is that it doesn’t by itself handle higher-level orchestration. It’s not a replacement for an agent’s reasoning logic or a developer’s application code. It just standardizes the interface to external functions/data. Developers still need to write the agent logic (or use LangChain/SK for that) and prompt the model to use tools appropriately. In summary, frameworks address the application logic layer and often hard-code integrations, whereas MCP addresses the integration layer itself as a standard.

Strategically, if MCP succeeds, we may see frameworks converge on it for their tool/plugin mechanism, much like how many IDEs all converged on LSP (Language Server Protocol) for their language support. But until then, they serve different needs: LangChain et al. help build solutions today with whatever integrations are available, and MCP is an emerging approach to make those integrations more interoperable.

MCP vs. OpenAI’s Function-Calling & Plugins Approach

OpenAI’s introduction of function calling (and the earlier ChatGPT plugins) tackled a similar problem: how can we let an LLM reliably call external code or fetch information? With function calling, developers describe functions (name, parameters, docstring) and pass these to the model; the model can then respond with a JSON indicating which function to invoke and with what arguments. ChatGPT plugins took this further by letting the model read an OpenAPI spec to autonomously call a web service. The difference is that these approaches are currently platform-specific and fairly closed. A ChatGPT plugin works in the ChatGPT environment and must be set up via OpenAI’s manifest format; it’s not a general protocol that other AI systems can inherently use. Function calling is a great developer feature, but it doesn’t provide a library of pre-made integrations – the developer still has to implement each function (which might internally call an API or database).

In contrast, MCP provides a catalog of functions (tools) at runtime that the AI can discover by querying the MCP server. It externalizes the function implementations into reusable servers. One could say OpenAI function calling is model-driven integration (the model is given functions to call) whereas MCP is platform-driven integration (the platform offers endpoints the model can use). Notably, these can work together: you could use function-calling within an MCP client. For example, an OpenAI GPT-4 model could be used as the AI agent, with a Semantic Kernel layer converting MCP tools into function calls that GPT-4 understands.

Strategically, OpenAI’s approach and MCP are somewhat competing visions. If OpenAI’s plugin ecosystem expands (with many third-party APIs exposed via OpenAPI/manifest and models that can read them), one might question the need for MCP. However, OpenAI’s system is not an open standard; it’s tied to OpenAI’s models and interfaces. MCP, being open and model-agnostic, could theoretically be adopted by many (including OpenAI models via adapters).

The risk is a fragmented landscape

We might end up with multiple “standards”. E.g., Anthropic pushing MCP, OpenAI pushing their plugin format, maybe others like Google with their own tool API. That would be reminiscent of past tech wars (for example, Betamax vs VHS or HD-DVD vs Blu-Ray in format wars, or multiple browser plugin APIs before common standards emerged). The hope for developers is that the industry coalesces around one approach or at least makes them interoperable (as we see with SK bridging them). In terms of capability, function calling and MCP’s tool calling are similar in latency and effect (both use structured JSON to invoke code). But MCP’s broader vision of “a standardized directory of capabilities” available to any AI agent goes beyond what OpenAI alone has done so far. It’s an attempt to create an ecosystem rather than a single-vendor feature.

Historical Parallels

MCP’s journey is analogous to several past standardization efforts. The closest parallel (mentioned by many) is the Language Server Protocol (LSP) in software development. Before LSP, code editors had to each integrate separately with each programming language’s tooling, leading to M×N complexity. LSP introduced a standard protocol between development environments and language analyzers, turning that into M+N – exactly what MCP aims to do for AI and data sources.

LSP succeeded because it met a genuine need and had broad support (Microsoft, open-source communities, etc.), eventually becoming ubiquitous in IDEs. Another parallel is ODBC (and JDBC) for databases: once upon a time, every application had to have custom drivers for Oracle, MySQL, SQL Server, etc., until ODBC standardized the interface. That standardization greatly reduced the friction of building database-backed applications and is cited as an inspiration for MCP’s potential impact). We can also look at OpenAPI itself – by providing a common format to describe APIs, it enabled a whole ecosystem of tools (documentation generators, client SDKs, testing tools) that wouldn’t exist in a world of completely bespoke APIs. MCP could similarly enable a new ecosystem of AI-aware integration tools (e.g., marketplaces of MCP servers, or auto-generated connectors as proposed by Speakeasy). On the hardware side, USB and USB-C show that unification can take hold even if it takes time – today, one USB-C port can connect storage, displays, power, etc., simplifying life, which is the kind of “single port for everything” outcome MCP advocates for AI.

Of course, not all standards succeed.

In the early web, SOAP/WSDL web services attempted to standardize integrations but were seen as overly complex, and eventually the simpler REST/JSON approach prevailed. There’s a cautionary tale there: MCP will need to keep the barrier to entry low and demonstrate simplicity, otherwise developers may stick with simpler (if less elegant) approaches. The current design of MCP seems mindful of this, using lightweight JSON and existing transports rather than inventing heavy new infrastructure. Still, its ultimate adoption will depend on whether the community sees it as solving more problems than it creates – much like how TCP/IP beat out the more complex OSI network stack because it was practical and already widely adopted.

4. Potential Risks & Downsides

No technology is without risk, and MCP faces several challenges:

Engineering Complexity & Overhead

Introducing MCP means adding extra components to the system – an MCP server layer and a client adapter. This indirection can introduce performance overhead (each tool call is an out-of-process RPC rather than an in-process function call) and potential points of failure. In high-throughput scenarios, the serialization/deserialization and context-switching could become a bottleneck if not optimized.

There’s also the token budget overhead: data fetched via MCP has to be injected into the model’s context window. Large contexts or many calls could inflate token usage (and cost) compared to a scenario where the data was perhaps already available to the model. That said, these overheads are generally manageable with streaming and proper design, and similar overhead exists in any external tool use – it’s more about adding another service to maintain. If an MCP server crashes or has a bug, the AI loses that capability; a tightly integrated approach might avoid that failure mode. Mitigating this risk will require robust implementations and perhaps tooling to monitor and restart MCP services as needed (much like one manages microservices).

Performance tuning might also be needed for very latency-sensitive tasks; e.g., in scenarios requiring dozens of rapid-fire tool calls, developers will need to architect carefully (perhaps batching queries or pre-loading certain context as resources rather than calling tools one by one).

Adoption Barriers

As with any proposed “standard,” one major risk is lack of adoption. If MCP fails to gain a critical mass of users (developers or companies), it could end up as an interesting but niche solution. Developers might perceive it as extra overhead to learn a new protocol when they could just call an API directly. The incentive to adopt increases with the availability of useful MCP connectors. Early in its life, MCP might suffer from a bootstrap problem: limited connectors mean less reason to use it; limited users mean fewer contributors writing new connectors. Anthropic’s initial open-sourcing of many connectors and SDKs is intended to jump-start this ecosystem, but it will need continuous momentum. There’s also an education and awareness hurdle – convincing the developer community (and decision-makers in enterprises) that MCP is worth standardizing on. It may take more case studies of MCP simplifying a project or being adopted by big industry players to move the needle.

Ecosystem Fragmentation (Strategic Risk)

A worst-case scenario would be the emergence of multiple incompatible “standards” for AI tool integration. For instance, if OpenAI, Anthropic, Google, and open-source all push different protocols (or even just cling to their own frameworks), developers and companies could be left in a confusing landscape. Some fragmentation already exists: OpenAI’s plugins, proprietary internal solutions, and now MCP. If each major AI provider has its own method, tool providers (like a company offering an AI-accessible service) might have to build multiple adapters – an outcome as bad as the original problem. To avoid this, either one approach wins out or they converge.

MCP’s open nature could allow it to be the convergence point, but it will require those players to see value in not reinventing the wheel. Market dynamics and even competitive strategy play a role here: a company might resist adopting a standard they didn’t create unless pressured by users or industry consortiums. There’s also the risk that big cloud vendors try to build similar capabilities natively into their platforms, bypassing MCP. For example, Microsoft could integrate tool plugins deeply into Azure’s AI studio in a non-MCP way – though interestingly they seem to be supporting MCP at least in Semantic Kernel as of now. If MCP remains mostly an Anthropic-centric tool, others might implement something like it under a different name, leading to parallel ecosystems and confusion for developers (reminiscent of the early days of container orchestration where Docker, Mesos, Kubernetes all vied until one won).

Over-Engineering & Unproven Needs

There’s a subtle strategic risk that MCP might be an over-engineered solution to a problem that simpler approaches could solve. If the industry finds that a combination of OpenAPI specs, some conventions, and existing tools (like function calling or prompt engineering) can achieve sufficiently integrated AI, then a whole new protocol might be seen as unnecessary complexity.

We have to ask: Were AI integrations truly hard enough to justify MCP?

Proponents would argue yes – the lack of real-time data access and the tedious work of integration have been major pain points. But skeptics might say that each major AI platform was already addressing this (OpenAI with plugins, Microsoft with Teams AI plugins, etc.), and an independent standard might not gain traction if it’s seen as duplicating those efforts. There’s also the risk that MCP doesn’t cover some future needs or is too rigid. For instance, if new kinds of model interactions or data modalities arise (say streaming video understanding or complex multi-step transactions), the protocol needs to evolve to handle them. Over-engineering can also manifest in trying to do too much: if MCP attempted to handle every possible nuance of tool integration it could become bloated. The current design seems fairly minimal (tools, resources, prompts as the three pillars), but it will require prudent stewardship to keep it from succumbing to feature creep or complexity that turns away would-be adopters.

Misaligned Incentives

Another strategic consideration is whether companies feel comfortable relying on an open standard rather than their own controlled solution. Some companies prefer to “own” their integration layer (for reasons like monetization, data control, or strategic differentiation). Convincing them to support MCP might require assurance that it won’t undercut their interests. The vendor-neutral positioning of MCP (open-source, Apache-licensed presumably) helps, but one can imagine scenarios where a vendor might add proprietary extensions leading to partial incompatibilities (similar to how browser vendors sometimes extended HTML/CSS in incompatible ways in the past). This could create subtle lock-in or fragmentation even if the base is standard.

In short, MCP’s risks range from technical (performance and complexity) to social (adoption and cooperation). These downsides don’t negate MCP’s promise, but they highlight that success is not guaranteed – it will require careful engineering to minimize overhead and strong community-building to avoid isolation.

5. Overall Verdict - Groundbreaking or Over-Engineered?

MCP can be seen as a groundbreaking advancement in that it attempts to do for AI tooling what other successful standards did in their domains – unify and simplify. The enthusiasm from early users and analysts is notable, with some calling it “a game-changer for AI integration” and drawing parallels to transformational standards like LSP and ODBC. There is a clear visionary quality: if every AI model and every data source spoke MCP, hooking up a new integration could be as simple as plugging in a USB device – no fussing with custom code or prompts, things just work. This could unlock a new level of AI capability, where complex multi-system workflows are handled by AI agents seamlessly. Imagine AI copilots that can on-the-fly connect to any enterprise system via a standard adapter, or personal AI assistants that aggregate info from all your apps using one protocol – that’s the future MCP is pointing toward. From that perspective, MCP is indeed groundbreaking.

On the other hand, we must acknowledge the skeptical view: MCP might be an elegant solution looking for a problem. Some might argue that we’ve been integrating AI with tools decently well so far with existing methods – after all, countless AI applications are already using REST APIs, vector databases, and plugins without needing a new protocol. If those methods continue to evolve (for example, OpenAI’s plugin ecosystem grows, or simpler frameworks emerge), MCP could struggle to justify itself. It wouldn’t be the first time a technically superior standard failed due to timing or network effects.

History provides both inspiration and caution

TCP/IP and HTTP show how open standards can revolutionize industries, while things like the Semantic Web (RDF, OWL) show that even a sound idea can overreach and see slow uptake because simpler techniques work “well enough.” MCP’s fate will likely depend on a few key factors:

Cross-Industry Support

If multiple AI providers (Anthropic, OpenAI, perhaps open-source LLMs via projects like LangChain or Semantic Kernel) all support or at least allow MCP, it will gain legitimacy. Neutral stewardship (maybe a foundation or working group) could help here. Success stories from big players adopting it will also build confidence.

Developer Experience

MCP needs to be easy and pleasant for developers. Early signs (SDKs, examples, the ability to have Claude automate connector creation) are positive. If spinning up an MCP server is as easy as writing a few lines (as shown in examples) and if using one in an app is straightforward, developers will be more inclined to try it. Conversely, if it’s perceived as cumbersome or poorly documented, they won’t bother.

Killer Use Cases

MCP will benefit from flagship demonstrations of things that were previously very hard to do, now made easy. For example, an AI assistant seamlessly pulling info from 10 different corporate systems in one conversation – if MCP enables that and it blows people’s minds (and saves devs weeks of work), it will drive adoption. A counterpoint is if those use cases remain niche – if most AI apps only ever need 1-2 integrations, they might not feel MCP is necessary.

Community and Ecosystem

A vibrant community building and sharing MCP servers could tilt the balance. If, within a year, there are MCP connectors for “everything” (from Jira to SAP to Notion to Gmail), the proposition to a developer becomes very attractive: why write custom code when a ready connector exists? This was crucial in other standards. E.g., the reason USB succeeded was that device makers adopted it en masse, so consumers found everything they needed in that format. If major software providers or open-source projects start releasing official MCP connectors (as speculated, e.g., “every major API could publish an official MCP connector”), that would be a huge boon. The opposite risk is if only a handful of connectors exist and many have bugs or limitations; then devs will stick to tried-and-true custom integrations.

Competitive Response

If MCP is widely seen as a good idea, competitors might adapt rather than resist. OpenAI could, for instance, allow ChatGPT to consume MCP connectors (they did something analogous by adopting the function calling interface which is conceptually similar to MCP’s tool schema). Or various frameworks might abstract the choice of integration under the hood (so a developer uses LangChain and doesn’t even realize they are using MCP for certain tools). Such moves would make MCP succeed indirectly. However, if a big player sees MCP as a threat to their ecosystem lock-in, they might push a parallel solution and try to make it more appealing (e.g., “why use MCP when our official plugins do the job on our platform?”). That could hinder MCP’s universal adoption.

Full And Final Verdict

At this stage, MCP should be viewed as a high-potential innovation that addresses real pain points, but it’s not yet a guaranteed win. It has the hallmarks of something that could be foundational – much like USB-C, LSP, or TCP/IP in their domains, it offers unification, flexibility, and future-proofing. If successful, MCP would significantly advance the ease of building AI-rich applications and encourage a more open, interoperable AI ecosystem. However, it also bears the risk of being an over-engineered detour if the industry finds simpler or more siloed solutions “good enough” or if it fails to gain critical mass.

In my view, MCP is worth watching and even experimenting with. It’s a bold attempt to solve a “solvable problem” in a more elegant, scalable way, and it aligns with lessons from tech history that standardization can unlock innovation (just as common networking protocols unlocked the internet boom). Whether it becomes a de facto standard will depend on the next couple of years. If we see broad endorsement and a flourishing connector ecosystem, MCP could indeed be a groundbreaking advancement that we look back on as a turning point in AI integration. If not, it may join the pile of well-intentioned standards that never quite caught on, with developers sticking to less uniform but familiar methods.

Ultimately, the success or failure of MCP will be determined by adoption, real-world utility, and community trust. It’s a classic case of a technology that is technically sound and strategically promising – now the question is whether the industry will rally behind it. Only time (and the collective choices of developers and companies) will tell if MCP becomes the “USB-C of AI” or an interesting footnote in the evolution of AI tooling. The opportunity is there for it to be truly transformative if executed and adopted well.

Beyond MCP: Building Real-World AI Success

Ultimately, the Model Context Protocol is only as valuable as the mindset and momentum behind it. Whether you’re looking to bootstrap your AI journey on a tight budget or navigate major career pivots in tech, it helps to see how others have tackled similar challenges. If you’re curious about real-life stories, check out my blog posts on studying AI in Germany with limited savings or founding an AI startup after leaving Canada. You’ll find that even unconventional paths can lead to meaningful progress.

For a more structured approach, my Live Masterclass covers the fundamentals of AI leadership, while One-on-One Sessions dive deeper into personalized strategy for career transitions or technology adoption. And if your organization is wrestling with the nuts and bolts of implementing AI, you might explore our Tech Due Diligence or Corporate Learning programs. By combining practical tools like MCP with proven guidance on AI adoption, you can turn cutting-edge technology into tangible, long-term success.