Model Context Protocol (MCP) vs Function Calling vs OpenAPI Tools — When to Use Each?

Approximately 10 minutes.

• Model Context Protocol (MCP) is ideal for building portable, multi-tool, and multi-runtime AI agent systems, offering standardized discovery and invocation across various hosts and servers.

• Function Calling (from LLM providers) is best suited for app-local automations with low latency requirements, providing straightforward integration and validation for specific, contained actions.

• OpenAPI Tools leverage existing enterprise HTTP services documented with OpenAPI Specification (OAS), perfect for governed, service-mesh integrations where mature contracts and security schemes are critical.

• Security and governance considerations differ: MCP relies on host policy, Function Calling on strict validation and allowlists, and OpenAPI Tools on OAS security schemes and API gateways.

• A hybrid approach is often the most practical, combining the strengths of each paradigm to address diverse integration needs within a single project.

• Introduction
• Comparison Overview and Definitions
  • Comparison Table
• Strategic Considerations: Strengths, Limits, and Governance
  • Model Context Protocol (MCP): The Portable Standard
  • Function Calling: The Integrated Performer
  • OpenAPI Tools: The Enterprise Backbone
  • Ensuring Security and Governance
• Real-World Application & Strategic Decision Making
  • Ecosystem Signals and Adoption Trends
  • Actionable Steps for Choosing the Right Approach
  • Real-World Example: An AI Assistant for a Tech Company
• Conclusion
• References
• Frequently Asked Questions

As AI models become increasingly sophisticated, their ability to interact with the real world — by leveraging external tools, services, and APIs — is paramount. This capability transforms mere language models into powerful agents, capable of automating tasks, retrieving information, and executing complex workflows. However, integrating these external functions seamlessly and securely presents a significant architectural challenge. Developers are faced with a choice between several emerging paradigms, each with its own philosophy and sweet spot.

Among the leading contenders for enabling AI-driven tool interaction are the Model Context Protocol (MCP), Function Calling (as offered by major LLM providers), and the use of OpenAPI Tools. Understanding their nuances is crucial for making informed decisions that impact scalability, portability, security, and developer experience. This article delves into each, helping you navigate the landscape and choose the right approach for your project.

Before diving into the detailed comparison, here’s a quick overview of each protocol:

• MCP (Model Context Protocol): An open, transport-agnostic protocol that standardizes the discovery and invocation of tools/resources across hosts and servers. It is best suited for portable, multi-tool, multi-runtime systems.

• Function Calling: A vendor-specific feature where the model selects a declared function (defined by a JSON Schema), returns arguments, and your runtime executes it. This is best for single-application, low-latency integrations.

• OpenAPI Tools: Utilizes the OpenAPI Specification (OAS) 3.1 as the contract for HTTP services; agent/tooling layers can then auto-generate callable tools. It is best for governed, service-mesh integrations within enterprise environments.

Choosing the right tool for the job goes beyond just understanding what each does; it requires a deep dive into their operational benefits, inherent constraints, and how they handle critical aspects like security and governance. Each framework brings a unique set of advantages and challenges to the table, influencing everything from development velocity to long-term maintainability.

MCP stands out for its emphasis on standardization and portability. Its strengths lie in enabling standardized discovery of tools, facilitating the creation of reusable servers, and supporting sophisticated multi-tool orchestration across diverse environments. With growing host support from platforms like Microsoft Semantic Kernel and Cursor, and even plans for Windows integration, MCP is designed for scenarios where tools need to be shared and invoked across different hosts and servers without vendor lock-in. However, its sophisticated architecture means it requires running servers and hosts to implement specific policies for identity, consent, and sandboxing, adding a layer of operational complexity. Hosts must also manage session lifecycles and routing.

Function Calling, largely a vendor-specific feature from major LLM providers, excels in its simplicity and efficiency. It offers the lowest integration overhead, allowing for a fast control loop where the LLM directly suggests function calls to your application. This makes it ideal for rapid development and straightforward validation via JSON Schema. The primary limitations revolve around its app-local catalogs and vendor-specific nature, which can hinder portability and built-in discovery or governance beyond the immediate application context. Redefining functions for different LLM vendors becomes a necessary step.

Leveraging the widely adopted OpenAPI Specification (OAS), OpenAPI Tools are a natural fit for integrating with existing enterprise service estates. Their strengths include mature contracts for HTTP services, in-spec security schemes (like OAuth2 and API keys), and a rich ecosystem of tooling that can auto-generate callable tools for agent layers. This makes them excellent for situations demanding rigorous governance and integration within established service-mesh architectures. The key limitation is that OAS primarily defines HTTP contracts; it doesn’t inherently provide agentic control loops, meaning you will still need an external orchestrator or host to manage the flow of interactions.

Regardless of the chosen method, security and governance are paramount:

• MCP: Relies on the host to enforce policy, including allowed servers, user consent, per-tool scopes, and ephemeral credentials. Its platform adoption signals a future where registry control and consent prompts are deeply integrated.

• Function Calling: Requires developers to meticulously validate model-produced arguments against schemas; maintain strict allowlists of callable functions; and log all calls for auditability.

• OpenAPI Tools: Benefits from native OAS security schemes, the ability to leverage API gateways, and schema-driven validation to protect services. It’s crucial to constrain toolkits to prevent arbitrary requests.

Understanding when to deploy each solution requires considering your project’s scope, existing infrastructure, and long-term goals. The ecosystem’s current state also offers clues on where each approach thrives, providing context for making informed decisions.

• MCP: Gaining significant traction with Microsoft Semantic Kernel supporting both host and server roles, and Cursor integrating MCP for directory and IDE context. Microsoft’s intent for Windows-level support underscores its potential for broad platform integration, signaling a push towards pervasive AI agency.

• Function Calling: Widely adopted and available across major LLM APIs from providers like OpenAI and Anthropic, demonstrating similar patterns for defining functions and processing tool results. Its ease of use makes it a default for many direct LLM integrations where vendor lock-in is less of a concern.

• OpenAPI Tools: A staple in various agent stacks, with frameworks like LangChain (both Python and JavaScript versions) offering robust capabilities to auto-generate tools directly from OpenAPI specifications. This leverages existing API definitions for new AI capabilities, bridging traditional service architectures with modern LLM-driven agents.

When faced with the decision, consider these steps to align your choice with your project’s requirements:

1. Assess Your Integration Scope: Determine if your automation needs are app-local and simple, or if they span multiple runtimes, hosts, and complex enterprise services. A few actions within a single application with tight latency targets might favor Function Calling, while a sprawling ecosystem points towards MCP or OpenAPI Tools.

2. Prioritize Portability and Reusability: If your goal is to build tools that can be shared, discovered, and invoked across different agents, IDEs, or even operating systems, MCP offers superior cross-runtime portability. For exposing existing HTTP services with vendor-neutral contracts, OpenAPI provides a robust framework.

3. Evaluate Security and Governance Requirements: For highly regulated or enterprise environments with existing security protocols and a need for stringent validation, OpenAPI Tools, combined with an orchestrator, provide the strongest foundation. For platform-level trust, user consent, and dynamic access control, MCP‘s host-driven policy enforcement is key.

Consider a tech company building an advanced AI assistant to help employees across various departments. This scenario perfectly illustrates the hybrid nature of tool integration:

• For quick, internal automations: A developer might initially use Function Calling to enable the assistant to instantly look up a user’s GitHub issues or create a JIRA ticket within a specific project. This is fast, low-latency, and confined to a single application’s scope, ideal for rapid prototyping and deployment of isolated features.

• For integrating with existing microservices: The company’s vast array of internal APIs (for HR, finance, customer support), all meticulously documented with OpenAPI specifications, would be best exposed as OpenAPI Tools. An orchestrator would route user intents to these services, ensuring security and compliance through defined contracts and gateways, leveraging existing enterprise infrastructure.

• For a truly ubiquitous agent: If the goal is for the assistant to interact with desktop applications (e.g., calendar, email client), access local files, and securely invoke tools residing on different servers or even different operating systems, Model Context Protocol (MCP) would be the ideal choice. It provides the standardized discovery and invocation necessary for a portable, multi-runtime agent experience, allowing the assistant to function across various user environments.

Often, a hybrid pattern emerges as the most practical solution: OpenAPI defines the enterprise services, a subset of these might be mounted as function calls for rapid interaction in latency-critical product surfaces, and an MCP server could then expose other services or local tools for broader portability and platform-level integration.

The choice between Model Context Protocol (MCP), Function Calling, and OpenAPI Tools is not about identifying a single “best” solution, but rather about selecting the most appropriate one for your specific use case. Each offers distinct advantages tailored to different architectural needs, integration complexities, and performance targets. Function Calling excels in simplicity and speed for app-local automations, OpenAPI Tools provide robust governance for enterprise services, and MCP paves the way for truly portable, multi-runtime agentic systems. By carefully evaluating your project’s scope, security requirements, and desired level of portability, you can make an informed decision that empowers your AI agents to interact with the world effectively and efficiently.

MCP (Model Context Protocol)

• modelcontextprotocol.io
• Anthropic: Model Context Protocol
• MCP Docs: Concepts & Tools
• MCP Legacy Docs: Concepts & Tools
• GitHub: modelcontextprotocol
• OpenAI Apps SDK: MCP Server
• Semantic Kernel Adds MCP Support for Python
• Integrating MCP Tools with Semantic Kernel
• Cursor Docs: MCP Context
• Microsoft Learn: Semantic Kernel Concepts

Function Calling (LLM tool-calling features)

• OpenAI Docs: Function Calling Guide
• OpenAI Docs: Assistants & Function Calling
• OpenAI Help: Function Calling in the API
• Anthropic Docs: Build with Claude – Tool Use
• Claude Docs: Agents and Tools – Tool Use Overview
• AWS Bedrock: Claude Messages Tool Use

OpenAPI (spec + LLM toolchains)

• OpenAPI Specification 3.1
• Swagger Specification
• OpenAPI Specification 3.1 Released
• LangChain Python: OpenAPI Tools
• LangChain Python API Reference: OpenAPIToolkit
• LangChain JS (OSS): OpenAPI Tools
• LangChain JS Docs: OpenAPI Toolkits