servers / boolsai-signals
Boolsai Scan MCP server
communitystreamable_httpremotehealthyhealthy
Live tech-stack scan of any public site — vendors, account IDs, scripts, JSON-LD. 2 tools.
01Tools · 12
How to read this: tool names here are observed from a live tools/list handshake. The Risk label is a heuristic inferred from the tool name (write/destructive verbs), not from executing the tool — a conservative guess, not a verified capability. We never escalate risk from a description. Found one that's wrong? Tell us — we fix on report.
| Tool | Risk | Side effects | Approval |
|---|---|---|---|
| event_dossier 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. | unknown | unknown | unknown |
| universe_summary 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. | read | false | unknown |
| wayback_backtest 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. | unknown | unknown | unknown |
| scan_at_date 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. | read | false | unknown |
| ticker_history 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'). | read | false | unknown |
| signal_diff Compare two signal patterns side-by-side. e.g. 'how does PRICING_TIERS_ADDED compare to VENDORS_DETECTED_CHANGED on the live dataset?' Returns α, %pos, sample size, worst/best trades for each, plus delta. Pure D1, fast. | read | false | unknown |
| recent_events 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?' | read | false | unknown |
| farm_domain Bulk-farm a domain's historical wayback snapshots into our index. Use this when you need backtest history on a domain we haven't already farmed (i.e. wayback_backtest / domain_timeline return no data for it). Hits CDX → samples weekly → parallel-scans up to 50 snapshots via intel.boolsai.ai → inserts into wayback_intel_profiles. After farming completes you can call wayback_backtest or domain_timeline on the domain immediately. Cost: ~30-60s wall time, ~50 intel scans. | unknown | unknown | unknown |
| test_filter 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. | read | false | unknown |
| domain_timeline Week-by-week wayback diff timeline for one domain. Returns every detected stack change (additions / removals) with week date. Use this to see when a vendor was added/removed historically, e.g. 'when did adobe.com add Segment?' | read | false | unknown |
| find_signals 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. | read | false | unknown |
| signal_landscape ONE-SHOT cross-signal sweep. Computes α-vs-SPY stats simultaneously across event_type, detector, diff_field, severity, AND co_occurrence dimensions — returns the full landscape in a single response. Use this FIRST when you want to see where signal lives without having to call find_signals N times. Stateless, pure D1, no rate-limit risk, ~1s response. Cached per arg set for sub-100ms repeated queries. | read | false | unknown |
02Install & source
https://grep.boolsai.ai/mcp
remote_urlhttps://directory.boolsai.ai/mcp
remote_urlhttps://boolsai.ai/mcp
remote_urlhttps://signals.boolsai.ai/mcp
remote_url- repohttps://github.com/Boolsai-ai/mcp
- homepagehttps://boolsai.ai/mcp
- licenseMIT
- adoption1 stars · 0 forks
05Provenance & freshness
sourcesOfficial MCP Registry [p1]
last_checked2026-08-16 15:50Z
next_check2026-08-17 01:43Z
cadenceevery 10h
verifiedhandshake:skipped metadata:passed tools_list:passed handshake:passed metadata:passed handshake:skipped metadata:passed handshake:skipped metadata:passed handshake:skipped
index_statusindex — 8 unique facts >= 5
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