MCP vs function calling vs ChatGPT plugins: what API teams actually need in 2026
When teams start connecting APIs to AI agents, three terms arrive together and get used interchangeably: function calling, ChatGPT plugins, and the Model Context Protocol (MCP).
The article explores the distinctions between function calling, ChatGPT plugins, and the Model Context Protocol (MCP) in the context of AI agent development, emphasizing the importance of standardized protocols for API integration in 2026.
What happened
The article discusses the differences between function calling, ChatGPT plugins, and the Model Context Protocol (MCP) in the context of AI agent development. It explains how these technologies are distinct layers in the stack and how they can be used together to create more robust and portable AI integrations. The article also outlines how OpenAPI documents can be used to generate MCP servers and tool descriptions, and emphasizes the importance of using MCP to avoid duplication and ensure portability across different AI agents and clients.
Why it matters
The article is important for AI developers and API teams because it provides a clear understanding of the different technologies involved in AI agent development and highlights the importance of using standardized protocols like MCP to ensure portability and reduce duplication. This is particularly relevant in 2026, as the use of AI agents is becoming more widespread and the need for standardized integration approaches is growing.
- Function calling is a model capability that allows the model to emit structured calls to APIs.
- MCP is a standardized client-to-server protocol that enables tool discovery and execution across different clients.
- OpenAPI documents can be used to generate MCP servers and tool descriptions, reducing duplication and improving portability.
- The article emphasizes the importance of using MCP to avoid duplication and ensure portability across different AI agents and clients.
- API teams should focus on maintaining a tight, current OpenAPI 3.2 document and serving it as an MCP endpoint.
The bigger picture
The article's insights on MCP and OpenAPI integration can help API teams create more robust and portable AI integrations, reducing duplication and improving the overall efficiency of AI agent development.
The article provides a clear understanding of the different technologies involved in AI agent development and highlights the importance of using standardized protocols like MCP to ensure portability and reduce duplication. It emphasizes the importance of using OpenAPI documents to generate MCP servers and tool descriptions, and encourages API teams to focus on maintaining a tight, current OpenAPI 3.2 document and serving it as an MCP endpoint.
Technical details
- Function calling is a model capability that allows the model to emit structured calls to APIs.
- ChatGPT plugins were a legacy approach for integrating APIs with models, now largely deprecated.
- MCP is a standardized client-to-server protocol that enables tool discovery and execution across different clients.
- OpenAPI documents serve as a foundational source for generating MCP servers and tool descriptions.
- The article highlights the importance of using MCP to avoid duplication and ensure portability across different AI agents and clients.
FAQ
What is function calling?
Function calling is a model capability that allows the model to emit structured calls to APIs. It is a feature of a chat completion where the model decides to call a function based on the input provided.
What is the Model Context Protocol (MCP)?
MCP is a standardized client-to-server protocol that enables tool discovery and execution across different clients. It provides a defined transport for tools, resources, and prompts, allowing for consistent integration across various AI agents and clients.
How does OpenAPI fit into MCP?
OpenAPI documents serve as a foundational source for generating MCP servers and tool descriptions. They provide a machine-readable catalog of callable operations with typed inputs and outputs, which can be mapped to MCP tools.
Why is MCP important for API teams in 2026?
MCP is important for API teams in 2026 because it provides a standardized protocol for tool discovery and execution, reducing duplication and ensuring portability across different AI agents and clients. It allows for consistent integration and avoids the limitations of legacy approaches like ChatGPT plugins.
What should API teams focus on in 2026?
API teams should focus on maintaining a tight, current OpenAPI 3.2 document with real descriptions, enums, and examples. They should serve it as an MCP endpoint and treat tool descriptions like API design, ensuring consistency and portability across different AI agents and clients.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
Read the full story
Originally published by Dev.to AI on Oct 3, 2026.
Comments
Sign in to join the discussion.