test: comprehensive tests for model metadata + firecrawl config

model_metadata tests (61 tests, was 39):
  - Token estimation: concrete value assertions, unicode, tool_call messages,
    vision multimodal content, additive verification
  - Context length resolution: cache-over-API priority, no-base_url skips cache,
    missing context_length key in API response
  - API metadata fetch: canonical_slug aliasing, TTL expiry with time mock,
    stale cache fallback on API failure, malformed JSON resilience
  - Probe tiers: above-max returns 2M, zero returns None
  - Error parsing: Anthropic format ('X > Y maximum'), LM Studio, empty string,
    unreasonably large numbers — also fixed parser to handle Anthropic format
  - Cache: corruption resilience (garbage YAML, wrong structure), value updates,
    special chars in model names

Firecrawl config tests (8 tests, was 4):
  - Singleton caching (core purpose — verified constructor called once)
  - Constructor failure recovery (retry after exception)
  - Return value actually asserted (not just constructor args)
  - Empty string env vars treated as absent
  - Proper setup/teardown for env var isolation

agent/model_metadata.py

@@ -158,6 +158,8 @@ def parse_context_limit_from_error(error_msg: str) -> Optional[int]:
         r'(?:max(?:imum)?|limit)\s*(?:context\s*)?(?:length|size|window)?\s*(?:is|of|:)?\s*(\d{4,})',
         r'context\s*(?:length|size|window)\s*(?:is|of|:)?\s*(\d{4,})',
         r'(\d{4,})\s*(?:token)?\s*(?:context|limit)',
+        r'>\s*(\d{4,})\s*(?:max|limit|token)',  # "250000 tokens > 200000 maximum"
+        r'(\d{4,})\s*(?:max(?:imum)?)\b',  # "200000 maximum"
     ]
     for pattern in patterns:
         match = re.search(pattern, error_lower)