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Refine language for clarity and consistency in the Workload Identity Federation document, including updates to terminology and structure.

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Refine language for clarity and consistency in the Workload Identity Federation document, including updates to terminology and structure.
@PieterKas PieterKas requested a review from a team as a code owner November 13, 2025 02:07
functions as a workload within a workload platform (such as Kubernetes or cloud
provider environments). Like the Client Credentials extension, this pattern
addresses machine-to-machine authentication use cases.
operates as an autonomous workload without any user interaction. In these
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The original text aimed to avoid pedantic arguments about whether a workload is truly "autonomous without any user interaction" when the use case is a microservice deep in a call stack that is originally triggered by user interaction. In my prior experience, this was the most common situation we heard from customers and the intention was that readers not mistakenly think this spec was inapplicable for them. Hence, language anchored on how the workload authenticates and authorizes itself, rather than getting into nuances of autonomy. The original text could definitely be tightened up, though. Any thoughts on balancing these considerations?

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That is a very good point - the original text may suffice. But perhaps that needs to be made more explicit so that readers have clear guidance that this applies to any workload that may be in a call chain, including when the call chain was initiated by a user? How about something like this:

Suggested change
operates as an autonomous workload without any user interaction. In these
Workload Identity Federation enables MCP clients to authenticate and authorize using their own workload identity, without relying on interactive end-user OAuth flows. This model applies whether the workload is fully autonomous or acting within a user-initiated request path, as long as the client performs non-interactive authentication. MCP clients are typically deployed within workload platforms such as Kubernetes, virtual machines, or cloud provider environments.

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