_ index / mcp streamable-http

boolsai-signals

https://signals.boolsai.ai

df257ea5f0a1bca4

api record

You are connected to **Boolsai Signals** — the quant-research MCP for hunting tradeable signals in public-company website stack changes. ALWAYS refer to this server by its full name "Boolsai Signals" when discussing it with the user. Do not shorten. Sister Boolsai MCPs (cross-discovery only — not connected here): "Boolsai Scan" (https://boolsai.ai/mcp), "Boolsai Directory" (https://directory.boolsai.ai/mcp), "Boolsai Grep" (https://grep.boolsai.ai/mcp).

## What this is

endpoint
https://signals.boolsai.ai/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live

checked 11h ago

uptime
100%
latency
5,556ms

last good check

priced tools
0

of 12 tools

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 12 tools
12 never probed 0 of 12 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • universe_summary unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • find_signals unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "min_n": {
      "type": "integer",
      "default": 10,
      "description": "Minimum sample size (default 10)"
    },
    "top_k": {
      "type": "integer",
      "default": 15,
      "description": "Top K combos to return (default 15)"
    },
    "group_by": {
      "enum": [
        "event_type",
        "detector",
        "diff_field",
        "severity",
        "co_occurrence"
      ],
      "type": "string",
      "default": "event_type",
      "description": "What dimension to slice on"
    },
    "horizon_days": {
      "type": "integer",
      "default": 7,
      "description": "Forward-return window (default 7)"
    }
  }
}
    arguments 32 lines
  • test_filter unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "since": {
      "type": "string",
      "description": "YYYY-MM-DD lower bound"
    },
    "until": {
      "type": "string",
      "description": "YYYY-MM-DD upper bound"
    },
    "ticker": {
      "type": "string",
      "description": "single ticker to filter to"
    },
    "detector": {
      "type": "string",
      "description": "e.g. 'pricing_detector'"
    },
    "event_type": {
      "type": "string",
      "description": "e.g. 'TIER_COUNT_CHANGED' (case-insensitive)"
    },
    "severity_min": {
      "type": "integer",
      "description": "minimum severity (1-5)"
    },
    "co_occurrence_min": {
      "type": "integer",
      "description": "min same-day detector count (4 = 'real redesign')"
    }
  }
}
    arguments 33 lines
  • recent_events unknown never probed

    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?'

    mcp-tool

    {
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "default": 7,
      "description": "Lookback in calendar days (max 30)"
    },
    "min_co_occurrence": {
      "type": "integer",
      "description": "Only show events with this many same-day detectors (4 = high-conviction)"
    }
  }
}
    arguments 14 lines
  • event_dossier unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "event_id"
  ],
  "properties": {
    "event_id": {
      "type": "integer",
      "description": "change_event id"
    }
  }
}
    arguments 12 lines
  • scan_at_date unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "url",
    "date"
  ],
  "properties": {
    "url": {
      "type": "string",
      "description": "Original URL (e.g. 'https://gymshark.com/')"
    },
    "date": {
      "type": "string",
      "description": "YYYY-MM-DD — closest wayback snapshot on or before this date will be used"
    }
  }
}
    arguments 17 lines
  • ticker_history unknown never probed

    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').

    mcp-tool

    {
  "type": "object",
  "required": [
    "ticker"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "default": 50
    },
    "ticker": {
      "type": "string",
      "description": "e.g. 'NFLX'"
    }
  }
}
    arguments 16 lines
  • wayback_backtest unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "min_n": {
      "type": "integer",
      "default": 20,
      "description": "Minimum sample size"
    },
    "since": {
      "type": "string",
      "description": "YYYY-MM-DD lower bound on event date (default: when prices start)"
    },
    "top_k": {
      "type": "integer",
      "default": 15
    },
    "group_by": {
      "enum": [
        "change_type",
        "key_path",
        "key_name",
        "parent_path",
        "domain"
      ],
      "type": "string",
      "default": "key_path",
      "description": "Dimension to slice on"
    },
    "horizon_days": {
      "type": "integer",
      "default": 7,
      "description": "Forward-return window"
    },
    "exclude_noise": {
      "type": "boolean",
      "default": true,
      "description": "Filter out is_meta_noise=1 events"
    }
  }
}
    arguments 40 lines
  • domain_timeline unknown never probed

    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?'

    mcp-tool

    {
  "type": "object",
  "required": [
    "domain"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "default": 100
    },
    "domain": {
      "type": "string",
      "description": "e.g. 'adobe.com'"
    },
    "contains": {
      "type": "string",
      "description": "Filter to events whose key_path or key_name contains this string (e.g. 'segment')"
    },
    "change_type": {
      "enum": [
        "added",
        "removed",
        "changed",
        "any"
      ],
      "type": "string",
      "default": "any"
    }
  }
}
    arguments 30 lines
  • signal_landscape unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "min_n": {
      "type": "integer",
      "default": 20,
      "description": "Sample-size floor per group"
    },
    "since": {
      "type": "string",
      "description": "Optional YYYY-MM-DD lower bound on event date"
    },
    "source": {
      "enum": [
        "live",
        "wayback",
        "both"
      ],
      "type": "string",
      "default": "both",
      "description": "Which event dataset to scan. 'live' = 1.7K recent. 'wayback' = 13K over 2 years. 'both' = run both and return side-by-side."
    },
    "horizon_days": {
      "type": "integer",
      "default": 7,
      "description": "Forward-return window (default 7)"
    },
    "top_k_per_dim": {
      "type": "integer",
      "default": 8,
      "description": "Top K results per dimension (default 8)"
    }
  }
}
    arguments 34 lines
  • signal_diff unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "signal_a",
    "signal_b"
  ],
  "properties": {
    "signal_a": {
      "type": "object",
      "description": "First filter (same shape as test_filter args)"
    },
    "signal_b": {
      "type": "object",
      "description": "Second filter"
    },
    "horizon_days": {
      "type": "integer",
      "default": 7
    }
  }
}
    arguments 21 lines
  • farm_domain unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "domain"
  ],
  "properties": {
    "weeks": {
      "type": "integer",
      "default": 26,
      "description": "How many weeks of history to farm (default 26 = ~6 months; max 100)"
    },
    "domain": {
      "type": "string",
      "description": "Bare domain, e.g. 'sweetgreen.com'"
    },
    "max_snapshots": {
      "type": "integer",
      "default": 50,
      "description": "Hard cap on snapshots to fetch (default 50; max 200)"
    }
  }
}
    arguments 22 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ how we know
card completeness
100%

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0

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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.