Overview

Model Context Protocol (MCP): MCP is an open standard introduced by Anthropic in late 2024 for connecting AI models with external tools and data sources . It aims to replace the many fragmented, one-off integrations in AI applications with a single unified protocol . In Anthropic’s words, MCP provides a “universal, open standard” to bridge AI systems to content repositories, business tools, development environments, etc. . OpenAI’s documentation even analogizes MCP to a “USB-C port for AI” – a standardized plug that lets any LLM connect to any dataset or service without custom adapters . In essence, MCP’s role in the LLM ecosystem is to be a neutral interface that any model or application can use to access a wide range of tools (functions/actions) and data (context) in a consistent way.

OpenAI’s OpenAPI-Based Functions/Plugins: OpenAI has developed its own mechanisms for tool integration, centered around function calling and ChatGPT plugins. Function calling (launched mid-2023) lets developers define functions (with JSON schemas) that a model like GPT-4 can call during a conversation . This enables an OpenAI model to fetch external data, invoke APIs, or perform actions based on the user’s request . Separately, ChatGPT’s plugin system uses the OpenAPI specification as a contract: plugin developers host a RESTful API and provide an OpenAPI doc describing it, which ChatGPT uses to decide how to call the plugin’s endpoints. In practice, the plugin platform acts as an OpenAPI-driven proxy between the model and external services. For example, much of the internet runs on REST APIs, and OpenAI showed that GPT-4 can leverage an OpenAPI spec to learn how to call those APIs intelligently . OpenAI’s tools ecosystem (functions + plugins) thus plays the role of extending model capabilities within OpenAI’s platform – allowing models to retrieve real-time information, use proprietary services, or execute code on behalf of the user . Unlike MCP, this approach isn’t an open standard; it’s specific to OpenAI’s environment (ChatGPT and its API) . OpenAI anticipated that industry-wide standards would emerge for AI-tool interfaces and began working on an early attempt , but so far their solution remains largely proprietary.

Before MCP vs. After MCP: Left – Each AI application had to integrate separately with each tool via unique APIs. Right – With MCP, the LLM connects through one unified interface (the MCP client/server), which in turn can access various tools (Slack, Drive, GitHub, etc.) via their existing APIs. This standardization reduces the integration complexity from M×N to M+N .

Developer Integration

MCP Integration: From a developer’s perspective, MCP simplifies tool integration by defining a common client-server architecture . Developers who maintain external services or data sources can implement an MCP server for each system (for example, a “GitHub MCP server” that knows how to fetch issues, or a “Slack MCP server” that can send messages) . These servers expose a standard set of methods (per the MCP spec) to list their capabilities and execute tool calls. Meanwhile, AI application developers (the ones building chatbots, IDE assistants, etc.) implement an MCP client in their app that connects to each server. Upon initialization, the client and server perform a handshake and the client queries what tools, resources, and prompts the server offers . Tools are essentially functions or actions the LLM can call (e.g. “send_message” or “query_database”), resources are read-only data accesses (like retrieving a file or a knowledge snippet), and prompts are preset prompt templates for optimal tool use . The client then makes these tools/resources available to the model (often by translating them into the model’s native format for function calls, such as JSON definitions) . When the LLM decides to use a tool, the client sends the request to the server which executes the action and returns the result . Importantly, the MCP spec supports multiple implementation modes – an MCP server can run as a local subprocess via stdio or as a remote service communicating over SSE (Server-Sent Events) . This gives developers flexibility: tools can be local (for low-latency access to, say, a filesystem) or remote (for web services), all accessed through the same protocol. Integration effort for MCP involves adopting the MCP SDK (available in languages like Python, TypeScript, Java, etc. ) and either writing or deploying the appropriate MCP servers. The payoff is interoperability: a single MCP server can work with any MCP-compatible AI client, and a single AI app can leverage any tool exposed via MCP. In short, MCP’s design emphasizes extensibility and standardization – it defines a comprehensive protocol (with structured concepts like tools, resources, and shared context) for tool usage across many scenarios .

That said, MCP is a new technology, and some developers note that it introduces additional complexity compared to more direct solutions. For example, prior to MCP, a common approach was converting existing OpenAPI specs into OpenAI function calls – essentially teaching GPT models to call REST endpoints using automatically generated function definitions. Developers who already have REST APIs may find it simpler to stick with that approach than to stand up a new MCP server. One early adopter commented that they had been doing “OpenAPI tool calling” since 2023 and felt that adapting everything to MCP would require creating new backend services or adapters, which is “so much more to do compared to OpenAPI tool calling” in simple cases . The MCP ecosystem is rapidly growing with tools and SDK support, but integrating it may involve an initial learning curve (understanding the spec, running servers) beyond what a quick one-off function call might require.

OpenAI Function/Plugin Integration: OpenAI’s integration path is quite different in philosophy – it has been more incremental and platform-specific. Developers have two main ways to connect tools in the OpenAI ecosystem:

• Via ChatGPT Plugins: This route is designed for third-party services to integrate with the ChatGPT UI. A plugin developer must provide a HTTP API (typically RESTful) and an accompanying OpenAPI specification that describes the API endpoints and schemas. They also supply a manifest file with some metadata and authentication details. Once the plugin is registered, ChatGPT will include the plugin’s documentation (the endpoints, parameters, and usage instructions derived from the OpenAPI spec) in the model’s context, so the model knows what it can do . When a user’s query seems to require that service (e.g. “Book me a flight” might trigger the Expedia plugin), the model can call an endpoint by outputting a JSON formatted request which the ChatGPT system executes behind the scenes. From the developer standpoint, creating a plugin means designing a well-documented API (often a subset of an existing service API) and handling the requests from ChatGPT. This is essentially a proxy server model – the plugin host acts as a proxy between the AI and the service. Many developers used this approach to expose internal APIs to ChatGPT as well, but each plugin is a custom integration. There isn’t a unified developer API for multiple plugins at once; you build them case by case. On the plus side, using widely accepted OpenAPI standards means many existing services can be adapted with relatively minimal changes (often it’s repackaging an existing REST interface with proper spec and auth).
• Via Function Calling in the API: For those building their own applications (outside ChatGPT’s interface), OpenAI provided function calling in the Chat Completions API. Here, the developer programmatically defines one or more functions and their expected parameters (using a JSON Schema format) when sending a prompt to the model. The model can decide to invoke a function by name, returning control to the developer with a JSON of arguments, and then the developer’s code executes the actual function logic and returns the result back into the model’s context for completion. This design is lighter weight than a plugin – you don’t need a web server or OpenAPI document; you directly pass function definitions to the model at runtime. However, it’s limited to your own application’s scope. To scale this approach to many external APIs, one would have to define a large set of functions or dynamically generate them. In fact, OpenAI demonstrated how one could programmatically convert an OpenAPI spec into a set of function definitions for the model . With tooling or scripts (and examples in the OpenAI Cookbook), a developer can take, say, the Slack API’s OpenAPI file and auto-generate send_message, list_channels, etc. as callable functions in the model . The model doesn’t natively understand OpenAPI; the developer essentially acts as the translator, feeding the model the function schema extracted from the spec. This approach has been adopted in many custom agents and frameworks – it’s the basis of how frameworks like LangChain integrate external tools (they either wrap them as OpenAI functions or use older prompt-based methods).

From an API design principle standpoint, OpenAI’s tools integration is ad hoc rather than standardized. It gives developers the freedom to define any function or plugin, but each definition is specific to the context of use (and only understood by the OpenAI model you pass it to). There is no registry of functions or negotiation protocol – the developer must explicitly tell the model what’s available every time. This is in contrast to MCP’s more systematic approach (which has a handshake and discovery step built into the protocol ). The advantage of OpenAI’s method is simplicity for small-scale integrations: if you just need your model to call two internal functions, it’s trivial to define them in a few lines of code. The downside is scalability and reusability: if you want to connect dozens of tools or swap out the LLM backend (say use a different vendor’s model), you’ll have to repeat or rework these definitions for each case. In short, OpenAI’s function calling and plugin APIs are powerful and fairly easy to get started with in their own ecosystem, but they do not in themselves provide a cross-platform standard. They focus on enabling integrations within OpenAI’s ecosystem – which for many developers (especially those primarily using OpenAI models) is sufficient – while MCP focuses on an open, model-agnostic ecosystem that several companies and models can share.

End-User Experience

For the end user – someone interacting with an AI assistant – the choice of MCP vs. OpenAI’s native tools can subtly affect the experience in terms of responsiveness, capabilities, and how “seamless” the tool usage feels in conversation. Both approaches ultimately serve the same goal: allowing the AI to do more than just chat, by pulling in external information or performing actions. Here’s how they compare on a few key aspects of user experience:

• Latency and Performance: Any time an AI system uses an external tool, there’s an added step (or several) that can introduce delay. In OpenAI’s plugin scenario, when ChatGPT decides to call a plugin, it must send an HTTP request to the plugin’s server and wait for a response before continuing the conversation. This can introduce noticeable pauses – for example, if the API is slow or the network is laggy, the user might see a “Waiting for Plugin…” message in ChatGPT. With function calling via API, the loop is in the developer’s hands, but there is still an extra round-trip (model → your code → model) for each function execution. MCP was designed with performance flexibility in mind: because an MCP server can run locally in the same environment as the model (via stdio), calls to local tools can be very fast . For instance, a local filesystem lookup or database query via MCP might complete nearly instantly, streaming the results back to the model. MCP servers can also communicate via server-sent events (SSE) , which means partial results could be streamed. This could improve responsiveness for certain tasks (the model can start formulating an answer while data is still incoming). In contrast, OpenAI’s function calling currently doesn’t have a built-in streaming for function results (the function either returns a result or an error, then the model continues). In practice, for many simple tool uses (like a single API call), both approaches add only a second or two overhead, often acceptable in exchange for the functionality. But in complex workflows involving multiple calls, the chaining of tools might be faster under MCP’s orchestration, especially if some calls can be done in parallel or via streaming. OpenAI’s approach might require sequential calls managed by the developer/agent, potentially taking longer.
• Reliability and Robustness: In a conversation, nothing breaks immersion more than an error or a tool failing unexpectedly. Both MCP and OpenAI methods have to deal with tool failures (e.g., an API returns an error or a server is down). OpenAI’s plugins rely on the plugin developer to handle errors gracefully and return useful error messages; the model might have some guidance to parse those and respond to the user accordingly. If a plugin is not well-maintained (say an API changed but the spec wasn’t updated), the user could get a vague error or no result. With function calling, the developer can catch exceptions in code and decide what the assistant should do (perhaps apologize or try an alternative). MCP, because it standardizes the interface, could potentially standardize error handling as well – the protocol might define error formats or fallback behaviors. Additionally, MCP’s design requires the AI client to discover capabilities at start , so the model is less likely to call a nonexistent tool or use it incorrectly (it knows exactly what functions and parameters are available from the server’s self-description). In OpenAI’s case, the model also only knows what you define, but if definitions are incomplete or the model misinterprets them, it might attempt something invalid. Overall, reliability often comes down to the implementation quality: an MCP setup with well-tested servers and a robust client will handle errors gracefully, and an OpenAI function integration can be coded to do the same. One advantage of OpenAI’s closed ecosystem is that plugins approved by OpenAI underwent some review, and the function calling schemas are simple enough to test easily. MCP is newer and still evolving, so there may be more edge cases in the wild initially. But as it matures, the uniform protocol can improve reliability by reducing the chance of integration mismatches.
• Conversational Fluidity and Context: One of the promises of protocols like MCP is better maintenance of context across tool uses . For an end-user, this translates to the AI not “forgetting” relevant information obtained via tools and being able to smoothly carry on the dialogue. MCP explicitly separates the concepts of tools (actions) and resources (read-only context data) . This means an AI agent using MCP might fetch some data as a resource (for example, pulling a document or a list of records) and keep it as part of its working context. The user can then ask follow-up questions about that data without the AI having to call the API again, unless needed. Essentially, MCP encourages a paradigm where the AI accumulates and maintains useful context from tools in a session. By contrast, OpenAI’s function calling doesn’t natively manage long-lived resources – it’s up to the developer to feed the model any context it should retain (often by adding it to the conversation history as text). For example, if ChatGPT via a plugin retrieved a list of GitHub issues in one turn, in the next turn the model only knows about those issues if the previous answer (or the user’s prompt) included them. There isn’t an automatic memory of an object unless it’s textual. This can sometimes make the conversation feel less fluid, as the model might re-call an API it just called if it wasn’t given the result explicitly to remember. Some agent frameworks using OpenAI functions mitigate this by storing intermediate results in hidden state, but that’s custom logic. MCP’s two-way design (clients and servers) inherently supports maintaining such context on the side and providing it to the model as needed . Another aspect of fluidity is how natural the AI’s responses are when using tools. In ChatGPT’s UI, function calls and plugin actions are behind the scenes – the user only sees the final answer (or a message like “Fetching data…” interim). Both approaches can produce very natural responses, but the range of actions available can influence conversational flow. With OpenAI plugins, ChatGPT historically allowed a limited number of plugins to be active at once (the user had to enable 3-4 specific ones). If a user question needed capabilities from multiple domains not covered by those enabled plugins, the assistant was out of luck. With MCP, an agent could theoretically have dozens of tools available simultaneously (since it can query multiple servers). This means the AI can seamlessly combine functionalities – e.g., use a database tool and a math tool in the same session – which for the user feels like a more capable assistant. Of course, having many tools also risks the AI choosing the wrong one, but a well-designed MCP client can help by providing clear descriptions to the model. There’s anecdotal evidence that Anthropic’s Claude (with MCP) was able to juggle a higher number of tools than GPT-4 in early tests . For the user, the end result is that an MCP-enabled assistant might say “I found these 10 documents you have in Drive related to your question” and then immediately offer to analyze them, whereas an OpenAI-based assistant might need the user to explicitly invoke one plugin for Drive and another for analysis, or do it in separate turns. In summary, MCP’s richer protocol can lead to an experience where the AI feels more like a cohesive assistant that naturally blends fetched knowledge into the conversation. OpenAI’s approach, while extremely useful, sometimes shows its seams (e.g., the AI might ask permission to use a plugin or slightly rephrase the query to fit a function) because it’s working within a less contextual framework.

Use Cases in the Wild

Both MCP and OpenAI’s function/plugin approach are being applied in real products and projects, each playing to their strengths. Below we highlight some real-world use cases and integrations for each:

MCP in Action: Since its open-source release, MCP has attracted the attention of companies building AI-enhanced platforms and workflows:

• Enterprise Data Access: Anthropic reports early adopters like Block (Square) and Apollo have integrated MCP into their systems . This likely means AI assistants at those companies can securely interface with proprietary data sources (financial data, customer records, etc.) through MCP servers, ensuring that the AI has the latest info when answering questions or automating tasks. By using MCP, these companies can connect the AI to multiple internal tools with a consistent approach (rather than custom-coding each integration).
• Developer Tools and IDEs: Development tool companies are among the first to leverage MCP. For example, the code editor Zed, the cloud IDE Replit, the AI coding assistant Codeium, and Sourcegraph’s code search tool are all working with MCP . In these scenarios, MCP enables AI agents that assist with programming to pull in context from various sources: a Sourcegraph bot can fetch relevant repository files or code snippets via a Git/GitHub MCP server; Codeium or Zed can use MCP to access a developer’s local filesystem or project documentation. The result is an AI pair programmer that’s much more aware of the project’s context, able to answer questions like “What does function X do? (referring to actual code)” or “Run my test suite” by invoking the appropriate tools.
• Data/Content Retrieval: There are open-source MCP servers already for services like Google Drive, Slack, GitHub, Git, Postgres databases, and even web browsers . This means an AI assistant (any that supports MCP) can, out-of-the-box, connect to a Slack workspace to read or send messages, query a SQL database, retrieve files from Google Drive, or control a browser for web actions. For example, an enterprise could use an AI agent to automatically gather context from Slack and Drive to answer an employee query – all done through MCP standardized calls. Another example is an AI assistant in a customer support tool that, via MCP, pulls relevant tickets from a system or checks inventory in a database when formulating a response.
• Multi-Model and Open Source Agents: Because MCP is model-agnostic, it’s being used in some open-source AI projects and frameworks. OpenAI’s own ChatGPT Agents SDK (released in 2025) included support for MCP, so developers using OpenAI’s SDK can register MCP servers as tool providers . This means even GPT-4 (through the Agents SDK) could call MCP-provided tools. Meanwhile, the community has built bridges such as MCP-to-OpenAPI proxies and OpenAI-compatible endpoints that translate function calls into MCP actions. These bridges allow an OpenAI or other OpenAPI-based model to use MCP servers as if they were regular REST APIs, which is useful for experimentation. We also see specialized MCP servers like BrowserMCP (to let an AI control a web browser) and others for niche domains (e.g., an “arXiv MCP server” for scientific paper search) . The range of these use cases showcases MCP’s flexibility – from enterprise integrations to developer tools to community-driven extensions – all sharing one protocol. It’s early days, but the momentum suggests MCP could become the de facto way to plug AI assistants into the digital world around them, especially in scenarios where multi-tool orchestration and cross-vendor compatibility are important.

OpenAI’s Tools in Action: OpenAI’s function calling and plugin ecosystem have been in the wild longer (since 2023) and have already made a splash in consumer-facing AI applications:

• ChatGPT Plugins for Consumers: Perhaps the most visible use cases are the plugins available in ChatGPT itself. OpenAI partnered with companies to launch plugins that let users extend ChatGPT’s capabilities. For example, the Expedia and Kayak plugins allow the AI to search for travel itineraries and prices; the OpenTable plugin lets it make restaurant reservations; the Instacart plugin can help order groceries . One standout is the Zapier plugin, which connects ChatGPT to over 5,000+ other apps . Through Zapier, a user could have ChatGPT update a Google Sheet, send an email via Gmail, post a message in Slack, and more – all in one conversation. This essentially turned ChatGPT into a hub that can operate on many web services (Zapier acts as the universal proxy for those actions). Another popular plugin is Wolfram Alpha (for complex calculations and factual queries), which gave ChatGPT a kind of “math and knowledge superpower” – users could ask advanced math or get up-to-date factual answers, and the model would call Wolfram behind the scenes for accurate results . These consumer plugin use cases highlight how OpenAI’s approach allowed specific high-value integrations to enhance ChatGPT: travel planning, shopping, productivity (e.g. the Slack plugin allowed sending messages or fetching info from one’s Slack workspace), and so on. Users interact with ChatGPT normally, and when a plugin is needed, the model’s answer will incorporate the plugin’s results (often with a brief note, like citing the source or saying “according to Expedia…”). It feels like the AI has “apps” it can use – which was a novel experience for users and greatly expanded ChatGPT’s utility beyond what’s in its training data .
• Enterprise and Custom Applications: Beyond the ChatGPT UI, many companies have built custom solutions using OpenAI’s function calling to integrate AI with their own tools. For instance, a customer support chatbot might use function calls to fetch a user’s account details from a database and recent support tickets from an internal system, then answer the user’s question with that context. Financial firms have used GPT-4 with functions to retrieve real-time stock data or portfolio information via their APIs so that the AI can provide up-to-date financial advice. A concrete example is Morgan Stanley’s internal advisor assistant, which (as reported in early 2024) used OpenAI tech to answer financial advisors’ queries by fetching information from the firm’s knowledge base – essentially a retrieval plugin tailored to their data. Similarly, e-commerce sites have experimented with AI agents that can take actions like applying discount codes or checking inventory (via function calls to their back-end). While these specific examples might not all be public, they align with the documented use cases of OpenAI’s tools: whenever you have a well-defined API or function, you can plug it into the model. Many developers used frameworks like LangChain or Microsoft’s Guidance to create multi-step agents that plan and invoke multiple OpenAI function calls to accomplish user goals (for example, planning a travel itinerary might involve calling a flight API, then a hotel API, then a mapping API in sequence). Essentially, OpenAI’s approach has been a catalyst for tool-using AI agents across industries – if an action or data retrieval can be formalized in an API, you can bet someone has tried hooking GPT-4 up to it. The caveat is that these remain siloed solutions (each built for a specific set of functions). For enterprise-grade deployments, concerns like authentication, rate-limits, and compliance also come into play, which developers handle at the integration level (e.g., ensuring the AI doesn’t call a function it shouldn’t, or that all calls are logged). OpenAI has provided some help here (like user-level API keys for plugins), but much is left to the implementer.
• Hybrid Approaches: It’s worth noting that OpenAI’s tools and MCP are not mutually exclusive in practice. We are seeing scenarios where an OpenAI model is used in conjunction with MCP. For example, a company might primarily use GPT-4 for its reasoning capabilities but use MCP to interface with tools – effectively treating MCP as the tool layer. In fact, with OpenAI’s Agents SDK supporting MCP servers, a developer can have GPT-4 list and call MCP-provided tools as if they were normal OpenAI functions . This hybrid approach might become more common: developers get the benefit of OpenAI’s powerful models and the benefit of MCP’s standardized tool ecosystem together. Similarly, other AI systems (like Google’s Gemini or open-source models via wrappers) can be integrated through their own function calling interfaces to MCP. The end goal across these use cases is clearly an AI that can seamlessly operate in the user’s world – whether that’s by following the “rules” OpenAI set (with plugins/functions) or via an open protocol like MCP.

Conclusion

Comparative Strengths: MCP and OpenAI’s OpenAPI-driven functions represent two philosophies for extending LLM capabilities. MCP’s strength lies in its standardization and neutrality: it was built as an “AI-native” protocol from the ground up, learning from early tool-use experiences to create a comprehensive, extensible interface . It treats tool use not just as API calling, but as a richer interaction with resources and shared context, which is ideal for complex applications that may involve many tools or data sources simultaneously. MCP is also vendor-agnostic – any model or platform can implement it – making it attractive for organizations that want flexibility and to avoid vendor lock-in. It’s backed by a major AI lab (Anthropic) and has buy-in from a community of tech companies, which suggests it will be well-maintained and evolve with input from many stakeholders . On the other hand, OpenAI’s function calling and plugin approach leverages the practicality and maturity of existing web standards (JSON, HTTP, OpenAPI) and the immediate needs of developers. Its key strength is ease of use for specific tasks: if you have a single API to integrate and you’re using OpenAI’s model, you can do so quickly and get value right away. Over the past year, it has proven capable of enabling a wide range of real-world use cases, from consumer chatbots booking flights to internal AIs retrieving business data. OpenAI’s tools benefit from the tight integration with their models – GPT-4 was trained with some understanding of how to follow function call patterns, and ChatGPT’s UI is optimized for plugin use, which can mean a smoother experience out-of-the-box for those use cases.

When to Use Which: The choice between MCP and OpenAI’s native tools often comes down to the scope and goals of your project:

• If you are building a multi-faceted AI assistant (especially in an enterprise setting) that needs to interface with numerous systems, and you want it to potentially work across different AI models or platforms, MCP offers a robust solution. Its unified protocol can greatly reduce integration overhead in the long run (turning that M×N integration problem into M+N ), and it’s ideal when you anticipate scaling up the number of tools or switching out AI models. For example, a company creating an AI assistant for employees – one that fetches HR data, queries databases, controls IoT devices, etc. – might find MCP to be a future-proof choice that ensures the assistant can interface with whatever new tool comes along in a consistent way
• If you have a targeted use case with OpenAI’s models and need a quick, reliable solution, using OpenAI’s function calling or plugins might be the pragmatic choice. For instance, if you’re developing a customer service chatbot that just needs to pull info from two internal APIs and you know you’ll stick to using GPT-4, it’s probably faster to implement those as functions via the OpenAI API. You’ll benefit from the rich capabilities of GPT-4 and can fine-tune how it calls those functions without introducing an additional layer of complexity. Similarly, if you want to reach existing ChatGPT users (e.g. by offering a plugin in the ChatGPT plugin store), then OpenAPI + plugin is the way to go, since MCP isn’t part of ChatGPT’s consumer offering at this time.

In many cases, these approaches can complement each other rather than conflict. We may see ecosystems where MCP is used in the background, but an OpenAI model is driving the conversation – or where an OpenAPI spec is the starting point, and an MCP wrapper is built around it to make it universal. For now, OpenAI’s solution enjoys the advantage of a large user base and real-world testing (through ChatGPT’s millions of users and developers), whereas MCP brings a vision of interoperability that could benefit the wider AI industry if broadly adopted. As open standards like MCP mature, we can expect the gap to narrow – OpenAI might even join the standard or adapt its APIs to be MCP-compatible, as developers have been asking .

In summary, MCP vs. OpenAPI-based tools is not an either-or dichotomy but a trade-off between a long-term universal framework and a proven immediate toolkit. MCP shines for those building the next generation of AI assistants that need to work across many contexts and want a sustainable architecture. OpenAI’s functions/plugins excel for those who want results today within the OpenAI platform’s scope, leveraging the power of GPT models with minimal fuss. A technical reader should view MCP as an emerging infrastructure piece – one that could underpin many future AI integrations – and OpenAI’s proxy/function approach as a practical bridge that has already connected language models with the wider world in countless applications. Each has its ideal scenarios, and understanding both will be valuable as the ecosystem evolves.

Sources: The information above draws from official documentation and reports on both technologies. Anthropic’s announcement of MCP and the MCP spec documentation provide insight into MCP’s goals and design. OpenAI’s function calling guide and plugin documentation illustrate the OpenAPI-based approach , and observations from developers and analysts helped contrast the integration experiences. Both approaches continue to develop rapidly (as of 2025), so keeping an eye on updates from Anthropic and OpenAI is recommended as the landscape may shift with new standards or features.