fix(py): fix test failures and enforce type checking in CI (zero lint except samples)

[yesudeep]

fix(py): fix test failures and enforce type checking in CI

This commit addresses test failures caused by type annotation fixes and
enables strict type checking in CI.

CI Enforcement:

• Remove --exit-zero from uv ty check in python.yml to fail CI on
  type errors

Bug Fixes:

• Fix MCP server read_resource to convert AnyUrl to str before passing
  to resource actions (server.py)
• Add callable() check for action.matches in find_matching_resource
  to prevent NoneType errors with dynamic action providers (resource.py)

Test Expectation Updates:

• Update schema expectations for nullable types to use anyOf format
  instead of simple type field (Pydantic v2 behavior for int | None)
  • veneer_test.py: 10 test expectations
  • schema_test.py: 1 test expectation

Type Annotation Fixes:

• Fix incorrect nullable type annotations across plugins:
  • value: int = None -> value: int | None = None
  • Updates to Ollama, Vertex-AI, Google-GenAI, Anthropic, MCP,
    compat-oai, DeepSeek, xAI plugins and core genkit packages

Context:
Pydantic v2 generates {"anyOf": [{"type": "integer"}, {"type": "null"}]}
for int | None fields, while the old incorrect annotations int = None
produced {"type": "integer"}. This commit updates all test expectations
to match the correct schema output.

[gemini-code-assist]

Summary of Changes

Hello @yesudeep, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request systematically refines the Python codebase by focusing on type correctness and consistency. It introduces explicit type hints, updates data structures to leverage Pydantic models and enums, and standardizes API interactions across various plugins. The changes aim to make the code more robust, maintainable, and easier to understand for developers by reducing ambiguity in data types and function signatures.

Highlights

• Enhanced Type Safety and Consistency: Significant updates across multiple Python packages and plugins to improve type checking and type safety. This includes migrating from generic dictionaries to more specific Pydantic models, enums, and typing module constructs like Union and cast.
• Dependency and Package Management: The uvloop dependency has been added for non-Windows platforms in the core genkit package. The genkit-plugins-mcp package has been renamed to genkit-plugin-mcp for consistency across the project.
• Refactored API Integrations: Various plugins (MCP, Google GenAI, Ollama, Vertex AI, xAI) have seen their API integration logic refined. This includes more robust handling of model responses, improved schema conversions, and clearer client initialization, especially for streaming and tool-use scenarios.
• Improved Resource and Metric Handling: Changes in the Firebase and Google Cloud plugins enhance how resources are converted and how metrics are cached, leading to more precise data handling and better performance monitoring.

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive set of fixes to improve type checking and overall code robustness across the Python codebase. The changes are of high quality and include replacing magic strings with enums, adding assertions and None checks for better type safety, refactoring for clarity and maintainability, and updating tests to use real objects instead of mocks. These modifications significantly enhance the reliability and maintainability of the code. I have one minor suggestion regarding environment variable handling in a sample file to improve consistency.

[yesudeep]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a wide range of fixes to address linting and type-checking diagnostics across the Python codebase. The changes significantly improve type safety and code robustness by adding explicit None checks, replacing string literals with enum members, updating Pydantic models to modern standards, and using stricter types. The tests have also been updated to reflect these changes, using real type instances instead of mocks, which is a great improvement. I've identified a couple of instances where type errors were silenced instead of being fixed and have provided suggestions for the correct implementation.