perf: optimize hot paths with caching and O(1) operations

[herniqeu]

• Replace list.pop(0) with deque.popleft() for O(1) queue dequeue
• Cache compiled regex patterns in ResourceTemplate for URI matching
• Cache field info mapping in FuncMetadata via lazy property
• Throttle expired task cleanup with interval-based execution

These optimizations target high-frequency operations in message queuing, resource lookups, tool calls, and task store access.

Motivation and Context

How Has This Been Tested?

Breaking Changes

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation update

Checklist

• I have read the MCP Documentation
• My code follows the repository's style guidelines
• New and existing tests pass locally
• I have added appropriate error handling
• I have added or updated documentation as needed

Additional context

[dchou1618]

I guess this would be a breaking change? Setting a throttling limit on the cleanup may not resolve all expired tasks and is timing-dependent, but this makes sense if the tasks are accessed very frequently (though I'm not sure what the benchmarks on this look like). How often does this happen/are there use cases that would be addressed with throttling cleanup?

[dchou1618]

nit: iiuc, these changes optimize repeated calls to pre_parse_json. I'm not sure what the protocol is for optimization-related tests, but it could be useful to add a small benchmark in the description on a ~20 field pydantic model and call pre_parse_json repeatedly (50k-100k before vs after).