sakthivel-p-cse/codex/agui-dotnet-client-tools

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Expose client-side (frontend) tools to an AG-UI agent with the AG-UI .NET SDK — C# functions that run in the CLIENT app (read local state, GPS, UI, device APIs), where the client executes the call and returns the result so the run continues. USE FOR: declaring an AIFunction in the client and passing it via ChatOptions.Tools to AGUIChatClient; having the client execute a tool the model requested and feed the result back automatically; understanding why no UseFunctionInvocation is needed on the client (AGUIChatClient already invokes functions); what the server must do (UseFunctionInvocation + TerminateOnUnknownCalls) so it forwards an unknown/client tool instead of erroring. DO NOT USE FOR: tools that run on the server/backend (use agui-dotnet-server-tools); pausing for human approval/input before acting (interrupts / human-in-the-loop); plain chat (use agui-dotnet-streaming-chat); shared state, generative UI, multimodal, or protobuf.

Install

npx vskill@latest install sakthivel-p-cse/codex/agui-dotnet-client-tools

SKILL.md

The same SKILL.md was published earlier as ag-ui-protocol/ag-ui/agui-dotnet-client-tools.

AG-UI .NET — client (frontend) tools

Goal: let the model call a function that runs in the client app — to read something only the client has (location, local state, UI selection, a device API) — with the client executing the call and returning the result so the run continues to a final answer.

A client tool is an ordinary Microsoft.Extensions.AI AIFunction you attach to the request via ChatOptions.Tools. AGUIChatClient is itself a function-invoking client, so it runs the tool locally and sends the result back automatically — you do not wrap it in UseFunctionInvocation.

Install

bash
dotnet add package AGUI.Client

Microsoft.Extensions.AI supplies AIFunctionFactory, ChatOptions, and AITool. Run dotnet package search AGUI.Client --exact-match for the current version.

Declare a tool and pass it in options

Define the function in the client, wrap it with AIFunctionFactory.Create, and put it on ChatOptions.Tools:

csharp
using System.ComponentModel;
using AGUI.Client;
using Microsoft.Extensions.AI;
[Description("Get the user's current location from GPS.")]
static string GetUserLocation() => "Amsterdam, Netherlands (52.37°N, 4.90°E)";
using var httpClient = new HttpClient();
IChatClient client = new AGUIChatClient(new(httpClient, "http://localhost:5003"));
var options = new ChatOptions { Tools = [AIFunctionFactory.Create(GetUserLocation)] };
var messages = new List<ChatMessage> { new(ChatRole.User, "What's fun to do near me?") };
await foreach (var update in client.GetStreamingResponseAsync(messages, options))
{
Console.Write(update.Text);
}

When the model decides to call GetUserLocation, AGUIChatClient invokes it on this machine, returns the result to the server, and the run continues — the streamed text you print already reflects the location.

What the server must do

The server forwards a tool it doesn't own to the client instead of trying to run it. Register the server's chat client with function invocation and TerminateOnUnknownCalls so an unknown (client) tool ends the server turn cleanly and is surfaced to the client to execute:

csharp
builder.Services.AddChatClient(/* provider IChatClient */)
.UseFunctionInvocation(fic => fic.TerminateOnUnknownCalls = true);

The server declares none of the client's tools; it only needs to forward them.

Anti-patterns

  • Wrapping the client in `UseFunctionInvocation`. AGUIChatClient already invokes functions; adding another function-invoking layer double-handles the call and can execute it twice or strip the AG-UI tool routing. Pass the tool through ChatOptions.Tools and let the client invoke it.
  • A client tool that returns before the side effect it reports completes. The model continues the run the instant the function returns, so a tool that says "saved" while an async write is still in flight makes the model narrate a result that has not happened. Await the real work inside the function before returning.

Verify

  1. Run a prompt the model can only answer using the client tool. The function body executes locally (set a breakpoint or log inside it).
  2. The streamed answer reflects the tool's return value, and the run ends RUN_FINISHED in a single client call — you wrote no code to send the tool result back.

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