perf: parallelize MCP server initialization in get_all_tool_definitions

[JasonOA888]

Partially addresses #137

Problem:
MCP tool servers were being initialized sequentially in a for loop:

• tool-python (E2B sandbox): ~33s
• search_and_scrape_webpage (Serper): ~21s
• jina_scrape_llm_summary: ~17s
• Total: ~71s per task

With 1266 BC-EN tasks, this adds significant overhead to evaluation runs.

Solution:

• Refactored server connection logic into _get_server_tools() helper function
• Used asyncio.gather() to connect to all servers in parallel
• Expected savings: ~40-50s per task (parallel time = max of individual times, not sum)

Changes:

• libs/miroflow-tools/src/miroflow_tools/manager.py:
  • New internal async function _get_server_tools(config)
  • Parallel execution via asyncio.gather(..., return_exceptions=True)
  • Graceful handling of exceptions from parallel execution

Error handling preserved:

• Failed connections still add an error entry to results
• Exceptions are logged and handled without crashing the entire initialization

Benchmark impact:

• BC-EN (1266 tasks): ~40-50s × 1266 = ~14-17 hours saved per run
• BC-ZH (289 tasks): ~4 hours saved per run