Boolsai Signals

skills: {'id': 'universe_summary', 'name': 'universe_summary', 'description': 'Orient the agent: total events, tickers, date range, top event types, top detectors, price coverage, SPY benchmark status. Call this FIRST when starting research. Returns counts that let the agent reason about sample sizes before drilling in.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'find_signals', 'name': 'find_signals', 'description': "Automated pattern discovery — scans event_type × detector × diff_field × severity combinations and returns those with the strongest forward-return characteristics (α vs SPY, % positive, n). Use this when you don't have a specific hypothesis yet. Returns sorted by α at +7D descending. Filter by min_n to set a sample-size floor.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'test_filter', 'name': 'test_filter', 'description': "Compute α stats for an arbitrary filter expression. Use this to test a specific hypothesis (e.g. 'tier_count_changed on enterprise-SaaS tickers' or 'severity 5 events that happened on Mondays'). Returns n, mean/median raw and α returns at +1/+3/+7d, % positive, and the worst-loss trade.", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'recent_events', 'name': 'recent_events', 'description': "Live signal feed: events fired in the last N days (default 7). Returns each event with the predicted α range based on its event type's historical performance. Use this to surface 'what should I be looking at right now?'", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'event_dossier', 'name': 'event_dossier', 'description': 'Deep dive on a single event: full diff (added/removed values), surrounding price action (-3D to +14D), predicted vs actual α, links to wayback comparison. Use this to investigate a specific event flagged by find_signals or recent_events.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'scan_at_date', 'name': 'scan_at_date', 'description': 'Scan a URL as it appeared on a historical date via the Wayback Machine. Uses intel.boolsai.ai against the wayback-wrapped URL. Returns the same JSON shape as Boolsai Scan but for a historical snapshot. Use when investigating WHEN a vendor was added/removed.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'ticker_history', 'name': 'ticker_history', 'description': "All events fired on a single ticker, plus price action timeline. Use this to investigate one company's pattern (e.g. 'show me everything we caught on NFLX').", 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}, {'id': 'wayback_backtest', 'name': 'wayback_backtest', 'description': 'Run an SPY-benchmarked backtest on the WAYBACK historical event dataset (2+ years, 13K events) instead of the recent live event dataset (2 months, 1.7K events). Much bigger samples for statistical confidence. Group by change_type / key_path / domain.', 'tags': [], 'examples': None, 'input_modes': None, 'output_modes': None}; uptime_30d 1.0%; p95 214.7ms; conformance: pass

How to connect

https://signals.boolsai.ai/mcp

curl -X POST https://signals.boolsai.ai/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'