Use this tool before making any trading or allocation decisions. Signals are ranked
cross-sectionally with higher scores indicating higher predicted relative returns.

Parameters:
    top_n: Number of top-ranked instruments to return. Default is 0, which returns
           all ranked instruments in the universe. Must be >= 0.

Returns:
    A dictionary containing:
    - as_of: Effective date of the signals (YYYY-MM-DD).
    - generated_at: UTC timestamp when the signals were generated.
    - source_model: Name of the model architecture (e.g. LightGBM, Alpha158).
    - data_source: Underlying market data feed.
    - horizon_days: Prediction horizon in trading days.
    - checksum: SHA-256 integrity checksum of the signals payload.
    - signals: List of ranked instruments with rank (1..N), symbol, and score.

Use this tool to inspect the active universe of assets covered by QuantVibe before querying
specific signals or checking market coverage.

Returns:
    A dictionary containing:
    - universe: Sorted list of ticker symbols (e.g. ['AAPL', 'MSFT', 'NVDA']).
    - count: Total number of instruments in the universe.
    - as_of: Effective date of the asset universe configuration (YYYY-MM-DD).

Use this tool for system monitoring and sanity checks before consuming signals in automated workflows.
Verifies that the signals file exists, the SHA-256 checksum is valid, and the data is within acceptable age.

Parameters:
    max_age_hours: Maximum allowable signal age in hours before considering data stale.
                   Default is 24.0 hours.

Returns:
    A dictionary containing:
    - ok: Boolean indicating if the signals file is healthy and fresh.
    - age_hours: Current age of the signals in hours since generation.
    - max_age_hours: The freshness threshold applied.
    - path: Path to the signals file on disk.
    - source_model: Model that generated the signals.
    - n_signals: Number of valid signals in the payload.
    - as_of: Market date of the signals.