fix(mcpserver): preserve Annotated/Field metadata for dict[str, T] return types - #2939

Fixed the pre-commit failure — ruff-format wrapped a long return-type annotation in the test file. All other checks were already green.

Nice catch! Using original_annotation instead of type_expr preserves the Annotated metadata for Pydantic. Simple one-line fix with the right regression test covering both description and title metadata.

@maxisbey — this has been open since Jun 21 with no maintainer pass, so surfacing it. It should be a fast one:

It answers a TODO this repo already wrote. func_metadata.py currently has, at line 510 on main today:

# TODO: should we use the original annotation? We are losing any potential `Annotated`
# metadata for Pydantic here:
model = _create_dict_model(func_name, type_expr)

The answer is yes, and that is the whole change — passing original_annotation instead of type_expr, so Annotated[dict[str, T], Field(description=...)] keeps its metadata in the output schema. One line of source, plus a regression test covering both description and title. Fixes #2935.

State: +30/-3 across 2 files, all 27 checks green, still MERGEABLE. It also survived #3331 untouched — that PR reworked other parts of the same file, but this origin is dict branch and its TODO are unchanged on main, so the fix still applies as written. Happy to rebase for a fresh run if you want one.

Independent read: @itxaiohanglover looked at it on Jun 26 and reached the same conclusion about original_annotation vs type_expr.

No objection if you would rather resolve the TODO differently — if you prefer the metadata to be dropped deliberately and the comment removed instead, say so and I will close this. I mostly want the TODO to stop being ambiguous either way.

Thanks for the PR, and sorry it sat here without a proper review.

We're closing most of the open PR backlog. v2 is out and changed a lot of the SDK, so many older PRs no longer apply as written, and we're a small team that realistically doesn't have the capacity to work through the rest.

If this still matters to you on v2, the most useful thing you can do is open an issue (or comment on the existing one) with your use case and a repro. Hearing why it matters to you is what we use to decide what to prioritise.

_{AI Disclaimer}