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What is a Semantic Kernel plugin?
Microsoft Learn describes plugins as a way to encapsulate existing APIs into a collection an AI can use. In practical terms, a plugin groups functions that connect the AI application to capabilities already available in your codebase, services, or APIs. A function might retrieve a current value or carry out a task.
The call is an application-mediated handoff: the model selects or requests a function based on the available descriptions, Semantic Kernel routes that request to the corresponding function, and the result is returned to the conversation. The model can use the result to form its response, but your application remains responsible for executing and controlling the function. See Microsoft’s overview of plugins in Semantic Kernel.
Which plugin integration route should you choose?
Semantic Kernel documents three routes. Choose based on where the capability already lives, how it is described, and whether it needs to be shared—not on a universal ranking.
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| Route | Best fit | What to check |
|---|---|---|
| Native code | Capabilities in your application code, dependencies, or services. Microsoft recommends this route when getting started. | Use clear function and parameter descriptions, and follow the current SDK example for your language. |
| OpenAPI specification | Operations described by an OpenAPI document, particularly when an API integration needs to be reused across languages or platforms. | Inspect parameter names and request-body schemas; not every schema necessarily maps cleanly to arguments the model can supply. |
| MCP server | Capabilities exposed through an MCP server supported by Semantic Kernel. | Confirm current SDK support and the server setup for your target language. |
For the official route descriptions, see Plugins in Semantic Kernel. For API schema handling, see the OpenAPI plugin guide.
How do I create a plugin in Semantic Kernel?
For a native plugin, the basic workflow is to define the functions, add the plugin to the kernel, and configure function calling so the model can request them. Exact APIs vary by language and SDK version; use the current language-specific quick start rather than copying code from a different version.
- Define the functions. Create functions for the capability you want to expose. The quick-start examples use a class with methods marked as kernel functions; language-specific approaches differ. Describe each function and its arguments so the AI can select and call it appropriately.
- Add the plugin to the kernel. Register the functions with the kernel using the API for your language and SDK. The kernel holds the services and plugins used by the application.
- Enable function calling. Configure the relevant execution settings or invocation behavior in your application. When the model requests a function, Semantic Kernel dispatches it and returns the result to the conversation.
The Semantic Kernel quick-start guide demonstrates this flow with a light-control plugin: one function retrieves a light’s state and another changes it. For native-function authoring and descriptions, see Provide native code to your agents.
How do function descriptions affect plugin calls?
Descriptions are part of the interface between your application and the model, not decorative documentation. The model relies on the information Semantic Kernel makes available—including descriptions and, where applicable, reflection—to understand what a function does and how to call it.
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- Describe each input clearly, including what values are expected and any meaningful limits.
- State what the function returns so the model can use the result correctly.
- Identify side effects. Make clear whether a function only reads information or changes a resource, and specify what it changes.
These details help the model choose and form a call; they do not replace application-side validation or controls. See Microsoft’s native functions guidance.
How should retrieval and action functions differ?
Retrieval and task automation solve different problems, so their design concerns differ. A retrieval function may support a response grounded in current or application-specific information. A task function may change state—for example, turning a light on or off—and therefore deserves particular attention to its effects.
- For retrieval: consider caching where appropriate, or lower-cost intermediate summarization when it suits the data and application.
- For automation: consider a human approval step before consequential changes. Describe the affected resource and the action plainly so the function’s intent is clear.
These are design recommendations, not guarantees that an implementation is safe, accurate, or suitable for every use. The application must implement the validation and approval behavior it needs. Microsoft discusses both patterns in its plugin overview.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you check when importing an OpenAPI plugin?
Semantic Kernel’s OpenAPI importer can create plugin functions from a URL, file, or stream. The operation metadata—such as parameter names, descriptions, types, and schemas—is exposed to help the model form arguments. The quality and shape of the API specification therefore affect how reliably an operation can be called.
Check parameter names for collisions
The OpenAPI guide warns that duplicate parameter names can confuse argument selection or make some operations unavailable. Review the specification for repeated names before relying on imported operations.
Choose request-body handling deliberately
Dynamic payload construction is enabled by default in the documented guide. For complex schemas, the guide also describes disabling it in favor of a payload parameter. Inspect the operations your application needs and test the resulting calls against the actual API rather than assuming every request schema will map cleanly. See Give agents access to OpenAPI APIs for the importer’s options and details.
What does the kernel do?
The kernel is the central component that holds the services and plugins used by a Semantic Kernel application. Its exact setup and lifecycle guidance can depend on the language. In particular, Microsoft’s C# dependency-injection guidance recommends transient kernel instances because the plugin collection is mutable, and notes that creating a kernel is lightweight. Treat this as C#-specific guidance, not a universal lifecycle rule for every SDK. See Understanding the kernel in Semantic Kernel.
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