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domfarolino merged 2 commits into from
Jun 11, 2026
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Remove ModelContextClient for now #205

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02df1fa
Remove `ModelContextClient` for now
domfarolino Jun 10, 2026
07781f0
Update explainer
domfarolino Jun 11, 2026
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -411,7 +411,7 @@ As the WebMCP proposal continues to evolve with community and stakeholder feedba

- **Output schema**: Supporting structured `outputSchema` contracts (complementing `inputSchema`) to help LLMs reliably reason about the return values of tools. See [Issue #9](https://github.com/webmachinelearning/webmcp/issues/9).

- **User prompting and elicitation**: Exploring a way for a tool to prompt the user for confirmation when tools require explicit user authorization. This could be done by delegating to the agent and its harness, or by invoking native browser permission dialogue outside of the agent loop. See [Issue #165](https://github.com/webmachinelearning/webmcp/issues/165) and [Issue #50](https://github.com/webmachinelearning/webmcp/issues/50) for discussion about the [`ModelContextClient`](https://webmachinelearning.github.io/webmcp/#modelcontextclient) interface.
- **User prompting and elicitation**: Exploring a way for a tool to prompt the user for confirmation when tools require explicit user authorization. This could be done by delegating to the agent and its harness, or by invoking native browser permission dialogue outside of the agent loop. See [Issue #165](https://github.com/webmachinelearning/webmcp/issues/165) and [Issue #50](https://github.com/webmachinelearning/webmcp/issues/50) for discussion about the `ModelContextClient` interface.

- **Tool progress reporting**: For long-running tasks (e.g., batch processing or generating content), the agent may want a way to track a tool's progress. We are exploring how this intersects with the established [MCP Progress](https://modelcontextprotocol.io/specification/2025-11-25/basic/utilities/progress) specification.

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33 changes: 2 additions & 31 deletions index.bs
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Expand Up @@ -482,7 +482,7 @@ dictionary ToolAnnotations {
boolean untrustedContentHint = false;
};

callback ToolExecuteCallback = Promise<any> (object input, ModelContextClient client);
callback ToolExecuteCallback = Promise<any> (object input);
</xmp>

<dl class="domintro">
Expand Down Expand Up @@ -510,7 +510,7 @@ callback ToolExecuteCallback = Promise<any> (object input, ModelContextClient cl

<dt><code><var ignore>tool</var>["{{ModelContextTool/execute}}"]</code></dt>
<dd>
<p>A callback function that is invoked when an [=agent=] calls the tool. The function receives the input parameters and a {{ModelContextClient}} object.
<p>A callback function that is invoked when an [=agent=] calls the tool. The function receives the input parameters.

<p>The function can be asynchronous and return a promise, in which case the [=agent=] will receive the result once the promise is resolved.
</dd>
Expand Down Expand Up @@ -559,35 +559,6 @@ dictionary ModelContextRegisterToolOptions {
</dl>


<h4 id="model-context-client">ModelContextClient Interface</h4>

The {{ModelContextClient}} interface represents an [=agent=] executing a tool provided by the site through the {{ModelContext}} API.

<xmp class="idl">
[Exposed=Window, SecureContext]
interface ModelContextClient {
Promise<any> requestUserInteraction(UserInteractionCallback callback);
};

callback UserInteractionCallback = Promise<any> ();
</xmp>

<dl class="domintro">
<dt><code><var ignore>client</var>.{{ModelContextClient/requestUserInteraction(callback)}}</code></dt>
<dd>
<p>Asynchronously requests user input during the execution of a tool.

<p>The callback function is invoked to perform the user interaction (e.g., showing a confirmation dialog), and the promise resolves with the result of the callback.
</dd>
</dl>

<div algorithm>
The <dfn method for=ModelContextClient>requestUserInteraction(<var ignore>callback</var>)</dfn> method steps are:

1. TODO: fill this out.

</div>

<h3 id="declarative-api">Declarative WebMCP</h3>

This section is entirely a TODO. For now, refer to the [explainer draft](https://github.com/webmachinelearning/webmcp/pull/76).
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