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What “not rewriting every tool” actually means
There are two kinds of change you may want to insulate your tools from:
- Changing model providers: Keep tool methods in your application and use a provider abstraction to connect them to different AI services.
- Sharing tools across hosts: Make tools available through a standard protocol so separate AI clients can discover and invoke them.
Neither approach makes a tool universally plug-and-play. You still implement its behavior, decide who may invoke it, and operate the code that performs the work. The goal is to avoid coupling that implementation to one model provider or one host.
How tool calling works in a .NET assistant
Tool calling is an application-mediated loop. The model receives tool definitions and a user message, then may return a structured request naming a tool and its arguments. Your application invokes the corresponding code, sends the result back to the model, and continues until it can produce a response. The model does not directly execute your .NET method. Microsoft’s .NET tool-calling guide describes this flow and warns that models can hallucinate arguments that were not described in function definitions.
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That distinction matters for both design and security: treat a model-generated tool request as untrusted input. Validate arguments, enforce authorization in the application or server, and avoid assuming that a well-formed request is an approved one.
Use Microsoft.Extensions.AI when the main concern is changing providers
Microsoft.Extensions.AI provides .NET abstractions for AI provider interactions, including function calling. The documented building blocks include AIFunction, AIFunctionFactory, and FunctionInvokingChatClient. You can define .NET methods as functions and make them available to an AI interaction without making each method itself a provider-specific integration.
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FunctionInvokingChatClient can handle function invocation and the continuation back to the model. Microsoft lists Azure OpenAI, OpenAI, and Ollama among implementations, but capabilities differ by provider and model. Check the documentation for the exact model you plan to use rather than assuming that all support the same function-calling behavior.
Keep the tool layer separate
Put the work your assistant performs behind ordinary application methods or services: for example, retrieving an order, looking up an internal record, or creating a support ticket. Expose appropriate methods as AI functions at the model boundary. This leaves the actual business logic in your application rather than embedding it in provider-specific request code.
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Control which functions reach the model
Function definitions consume context tokens. Microsoft recommends registering fewer tools, using concise names and descriptions, and exposing only the functions relevant to the conversation. Some models support parallel function calls, and FunctionInvokingChatClient can use them when the underlying model supports them. Parallel behavior is not guaranteed across providers or models.
Use MCP when different hosts need the same tools
The Model Context Protocol standardizes a different connection boundary. In its architecture, an MCP host uses MCP clients to connect to MCP servers; clients can list and call the tools a server exposes. The official MCP C# SDK supports building .NET clients and servers.
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MCP is useful when tools should be discoverable by more than one compatible AI host or application. Instead of connecting each host to a separate provider-specific implementation, you expose a server through the protocol and let compatible clients interact with its tools. This creates a standard integration surface; it does not guarantee every host supports every capability or that tool semantics and permissions are interchangeable.
Choose the package for the server or client you are building
The official SDK repository describes distinct package roles. Package names and version guidance can change, so check the repository when selecting the version for a new project.
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| Package | Role described by the official SDK repository |
|---|---|
ModelContextProtocol.Core |
Client and low-level APIs |
ModelContextProtocol |
Most servers, hosting, and dependency injection |
ModelContextProtocol.AspNetCore |
HTTP-based MCP servers |
| Apps and Tasks extensions | Separate extension packages; check current SDK guidance for their specific APIs and compatibility |
As of Microsoft’s October 2026 v2 announcement, the SDK describes stateless behavior as the default, prefers protocol revision 2026-07-28, and lists target frameworks net8.0, net9.0, net10.0, and netstandard2.0. The announcement also identifies migration differences for users of experimental Tasks. These are version-sensitive details, not permanent requirements: verify the release notes and compatibility guidance for the SDK version and server transport you intend to use.
Combine the approaches when both kinds of portability matter
MEAI and MCP are complementary. A local assistant can use MEAI to connect provider-agnostic function calling to .NET methods. An MCP client can also discover tools on remote MCP servers and make them available to model function calling. In that arrangement, MCP standardizes access to tools across hosts, while MEAI provides an abstraction for the model interaction in your .NET application.
Use the fewest boundaries that satisfy the reuse you actually need. A local application with one host may need only MEAI. Tools shared among separate compatible hosts may justify MCP. Using both can help when you need both provider flexibility and host-to-tool interoperability, but adds integration work rather than removing it.
Choose based on deployment and operating costs
| Question | MEAI with in-process tools | MCP server |
|---|---|---|
| What portability does it address? | Changing AI provider integrations in a .NET application | Making tools discoverable to compatible clients and hosts |
| Where does the tool run? | In the application that invokes the .NET functions | On an MCP server accessed by clients |
| What do you need to operate? | Application-level tool invocation, validation, and authorization | Those tool concerns plus protocol, transport, server lifecycle, and authorization choices |
| What should you verify? | Function-calling and parallel-call support for the selected provider and model | Client/server protocol and SDK version compatibility, transport support, and host capabilities |
Both approaches also require attention to the model context budget: tool descriptions count toward token limits. For MCP, add the operational work of running and securing a server. For local function calling, execution remains in the application, but you must still validate the model’s requested arguments and enforce your own access rules.
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A practical decision path
- Keep tools local and switch providers? Define the methods as AI functions and use MEAI abstractions such as
AIFunctionandFunctionInvokingChatClient. Confirm that the provider and model you select support the function-calling behavior you need. - Let other compatible hosts use your tools? Implement an MCP server with the official C# SDK. Select the SDK package and transport that fit the server, and check version compatibility before deployment.
- Need both forms of reuse? Connect MCP-provided tools into the model function-calling flow in your .NET application, using MEAI where it fits your provider integration.
- For every exposed tool, review its scope. Keep definitions relevant and concise, validate arguments, and authorize each operation where it executes.
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