mcp server

Boolsai Signals

Quant-research MCP — tradeable signals from public-company website stack changes. 7 tools.

Description as published by the maintainer. Source

• version 1.0.0
• slowing
• retrieval

slowing — Most recent push to the repository was 2026-05-19. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.

What this server can do

12 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

domain_timeline(limit, domain, contains, change_type)

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?' Required: domain.

event_dossier(event_id)

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. Required: event_id.

farm_domain(weeks, domain, max_snapshots)

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. Required: domain.

find_signals(min_n, top_k, group_by, horizon_days)

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.

recent_events(days, min_co_occurrence)

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?'

scan_at_date(url, 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. Required: url, date.

signal_diff(signal_a, signal_b, horizon_days)

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. Required: signal_a, signal_b.

signal_landscape(min_n, since, source, horizon_days, top_k_per_dim)

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.

test_filter(since, until, ticker, detector, event_type, severity_min, co_occurrence_min)

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.

ticker_history(limit, ticker)

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'). Required: ticker.

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.

wayback_backtest(min_n, since, top_k, group_by, horizon_days, exclude_noise)

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.

Last successful function declaration observed on 2026-08-13. Source: https://signals.boolsai.ai/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://signals.boolsai.ai/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/Boolsai-ai/mcp
• Remote endpoint (streamable-http) — https://signals.boolsai.ai/mcp

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• Boolsai Scan — last commit 2026-05-19
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