Model Context Protocol Comparison: MCP vs Function Calling, Plugins, APIs

How does the Model Context Protocol (MCP) compare to other ways of connecting AI to tools? In this article, we break down how MCP stacks up against function calling, plugins, direct APIs, and agent frameworks. We’ll also explore real-world use cases for MCP and where this emerging standard might take us in the future.

MCP vs. Other Function Calling Approaches

To understand MCP's place in the AI ecosystem, it's helpful to compare it with other methods for connecting LLMs to external tools and services.

OpenAI's Function Calling

OpenAI introduced function calling capabilities in 2023, allowing developers to register functions with the API and have models like GPT-4 invoke them. This approach works as follows:

The developer provides a schema (name, parameters, description) for each function
The model decides whether to use a function based on the user's request
If using a function, the model returns a JSON object with the function name and arguments
The developer's code executes the actual function and feeds the result back to the model

While conceptually similar to MCP's flow, OpenAI's approach has several limitations:

It's tied specifically to OpenAI's platform and API format
Functions must be predefined for each session
There's no built-in support for persistent sessions with function providers
Each function call is essentially independent, managed by intermediate code

MCP addresses these limitations by providing a model-agnostic, persistent channel. Any MCP-compatible client can call any tool, and it can maintain an ongoing exchange with stateful context.

Interestingly, these approaches can be combined. For example, a developer could implement a generic call_mcp_toolfunction within OpenAI's function calling framework. The model would output which MCP tool to call, and the function would route that through the MCP protocol.

ChatGPT Plugins

ChatGPT plugins represented another approach to extending LLM capabilities. Each plugin defined its own API (usually via an OpenAPI spec) that ChatGPT could call. While innovative, plugins had several limitations:

Each plugin was a bespoke integration with its own API schema and authentication
Plugins were essentially one-off connections without persistent sessions
Only ChatGPT (or similarly privileged systems) could use these plugins
Authentication was handled in ad-hoc ways without a standardized flow

MCP improves on this model by providing an open standard any AI platform can adopt. It supports persistent connections, standardized authentication (OAuth 2.0), and isn't restricted to one vendor's ecosystem.

As one analysis puts it: ChatGPT plugins are like specialized tools in a closed toolbox, whereas MCP is an open-standard toolkit available to any developer.

Agent Frameworks (LangChain, etc.)

Before high-level protocols, many developers used agent frameworks like LangChain to connect LLMs with external tools. These frameworks provided abstractions where the model could output commands like "Action: search Google" that the framework would execute.

However, these approaches had limitations:

They were essentially hacks on top of the model's text interface
Different tools often used inconsistent formats or command structures
Integration was typically tailored to specific models
There was no unified standard for tool definitions or interactions

MCP addresses these issues by providing a single standard interface. Tools present themselves consistently, and any compliant client can mediate the model's interaction with them. This centralizes tool logic in the protocol rather than scattering it across code.

Direct API Integration

The most basic approach involves manually inspecting user queries or model responses, determining which API to call, executing it, and injecting results back into the model's context.

This works but doesn't scale well:

Every new API requires custom code
Each integration is brittle and model-specific
Integrating multiple APIs becomes exponentially complex
There's no standard way to handle errors or authentication

MCP solves these problems through standardization. Once a service has an MCP server, any compatible AI client can use it without additional integration work.

Real-World Applications of MCP

The true power of MCP becomes apparent when we consider real-world applications. Let's explore a few scenarios where MCP's unique features enable sophisticated AI assistants.

Integrated Development Environments (IDEs)

Consider an AI coding assistant in an IDE like VS Code or JetBrains. Using MCP, this assistant could:

Access project files via a file system MCP server
Run code via a code execution MCP server
Query documentation via a docs MCP server
Create tickets in issue trackers via a project management MCP server
Generate unit tests via a testing MCP server

All of these interactions would follow the same protocol pattern, making the assistant's capabilities easily extensible. New tools could be added without modifying the core AI integration.

When the user asks "Find performance bottlenecks in this code and create a ticket for each one," the assistant can orchestrate a complex workflow across multiple tools—profile the code, analyze results, create tickets—all through a consistent interface.

Enterprise Knowledge Workers

For knowledge workers in an enterprise setting, an MCP-enabled assistant could:

Search internal documents via a knowledge base MCP server
Access employee information via an HR database MCP server
Schedule meetings via a calendar MCP server
Generate reports via a data analysis MCP server
Share content via communication platform MCP servers

This creates a powerful productivity tool that integrates seamlessly with existing enterprise systems. As new tools are deployed, they simply need to implement the MCP interface to be accessible to the assistant.

For example, when asked "Prepare a quarterly sales report for my team and schedule a review meeting," the assistant can gather data from multiple sources, generate visualizations, create a document, and schedule the meeting—all through consistent MCP interactions.

Smart Home and IoT Control

Beyond professional contexts, MCP could transform how we interact with smart home devices. An MCP-enabled assistant could:

Control lighting via a smart home MCP server
Adjust thermostats via an HVAC MCP server
Monitor security systems via a security MCP server
Play media via entertainment system MCP servers

This provides a unified interface for complex home automation scenarios. For instance, "I'm going to bed" could trigger a sequence of actions across multiple systems—turning off lights, adjusting temperature, arming security—orchestrated through MCP.

The Power of Composability

What makes these scenarios particularly powerful is MCP's composability. Rather than relying on predefined workflows, an MCP-enabled assistant can dynamically combine tools based on the specific request.

For example, when asked to "Research recent papers on climate change, summarize the key findings, and create a presentation for next week's meeting," the assistant might:

Use a web search MCP server to find recent papers
Use a PDF processing MCP server to extract content
Use a summarization MCP server to distill key findings
Use a presentation MCP server to create slides
Use a calendar MCP server to schedule the presentation

This dynamic composition allows for emergent capabilities—the assistant can solve problems it wasn't explicitly programmed to handle by combining available tools in novel ways.

The Future of MCP

As MCP continues to evolve, several developments seem likely:

Expanding Ecosystem

The open nature of MCP will likely lead to a growing ecosystem of compatible servers and clients. We can expect to see:

MCP servers for popular web services and APIs
Language-specific MCP client libraries
MCP gateway services that aggregate multiple tools
Industry-specific MCP server collections

This ecosystem growth will make MCP increasingly valuable through network effects—each new compatible tool or client enhances the value of the entire ecosystem.

Enhanced Security and Governance

As MCP adoption grows, particularly in enterprise settings, we'll likely see enhanced security and governance features:

Fine-grained permission models
Audit logging and compliance features
Enterprise authentication integration
Data residency and privacy controls

These enhancements will make MCP suitable for sensitive environments where tool access must be carefully controlled.

Extension to Multimodal Models

While current MCP implementations focus on text-based LLMs, the protocol could be extended to support multimodal models that handle images, audio, and video. This might include:

Image analysis and generation tools
Audio processing and transcription tools
Video analysis and editing tools

Such extensions would bring MCP's benefits to a broader range of AI applications.

Standardization and Industry Adoption

As MCP proves its value, we may see formal standardization efforts and broader industry adoption:

Industry consortiums defining MCP extensions
Cloud providers offering MCP-compatible services
Enterprise software vendors implementing MCP interfaces
Operating systems integrating MCP as a standard feature

This would cement MCP's role as the universal interface for AI-tool interactions.

Challenges and Considerations

Despite its promise, MCP faces several challenges:

Competing Standards

MCP isn't the only attempt to standardize AI-tool interactions. Other frameworks and protocols are emerging, and consolidation will take time. The risk of fragmentation—multiple incompatible standards—remains real.

Performance Overhead

The additional layer of abstraction introduced by MCP comes with some performance overhead. For latency-sensitive applications, this could be a concern. Future optimizations will likely address this issue.

Security Risks

As with any protocol that enables automation, MCP raises security concerns. Malicious MCP servers could attempt to exploit vulnerabilities in clients, and compromised clients could misuse legitimate servers. Robust security practices will be essential.

Adoption Barriers

Despite its advantages, MCP faces adoption barriers:

Legacy systems may be difficult to adapt
Organizations may resist standardization efforts
Developers may prefer familiar, albeit less efficient, approaches

Overcoming these barriers will require compelling demonstrations of MCP's value.

Conclusion

The Model Context Protocol represents a significant advance in how AI systems interact with external tools and services. By providing a standardized, composable interface for function calling, MCP addresses many of the limitations of earlier approaches.

As we've explored in this series, MCP's client-server architecture, lifecycle management, dynamic discovery, and interoperability create a foundation for more capable AI assistants. These assistants can combine multiple tools to solve complex problems, maintaining context across interactions and adapting to user needs.

The growing ecosystem around MCP suggests a future where AI assistants seamlessly integrate with a wide range of digital systems, from web services to enterprise applications to smart home devices. This integration will unlock new capabilities and use cases, making AI more practical and valuable in our daily lives.

While challenges remain, the direction is clear: we're moving toward a world where AI systems can effectively leverage external tools and data through standardized protocols like MCP, dramatically expanding what's possible with AI assistants.

As developers, organizations, and users, we would do well to follow MCP's evolution closely. It may well become the universal language through which AI systems interact with the digital world—a development with profound implications for the future of human-AI collaboration.