"Signal" matching MCP tools:

• strategy-signal

  The Stall

  Technical analysis signal for any US equity, ETF, or crypto. Returns RSI(14), MACD(12/26/9), Bollinger Bands(20), volume trend, directional posture (STRONG_BUY/BUY/NEUTRAL/SELL/STRONG_SELL), and key price levels. Richer output than comparable services at $0.090. Free upstream: Yahoo Finance (equities), CoinGecko (crypto).

  Connector

• scan_slate

  infersports

  Scan today's whole slate in ONE call — each fixture with honest status + value/arb signal. The batch alternative to looping find_match → get_sharp_line per match. Returns every fixture in the filter with its status (finished is excluded from "live"), live score/clock, and a pre-computed value/arb signal; value/arb matches are sorted to the top and the list is truncated to ``limit`` (so truncation drops the quiet ones). Line movement is NOT included (that needs the opening lookup) — drill into a single fixture with get_opening_line. DETECTION ONLY / read-only. Args: sport: optional filter — "football" or "basketball". status: optional filter — "live" | "scheduled" | "finished". league: optional league filter — a name (fuzzy-matched, e.g. "World Cup") or an external id (lg_…). markets: optional — limit the value/arb scan to "1x2"/"asian_handicap"/"totals" (default all). period: optional — "full_time" or "half_time" (default both). min_edge_pct: value threshold for the per-match signal (default 1.0). min_margin_pct: arbitrage threshold for the per-match signal (default 0.0). only_signal: if true, return only fixtures that have a value or arb signal. format: odds format — decimal | hk | malay | american | indonesian | probability. limit: max entries to return, signal-first (default 20, max 100).

  Connector

• explain_decision

  OneQAZ Trading Intelligence

  Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log, (3) news causality context. Use this when an AI must present a structured "why" rather than a raw verdict. When to call: when the user asks "why is this signal bullish/bearish?". Prerequisites: identify the symbol via get_signals or get_latest_decisions first. Next steps: none (this completes the explanation chain). Caveats: `symbol` must match the per-symbol signal store filename (lowercase). Output is research evidence, NOT a buy or sell recommendation. Args: market_id: Market identifier (crypto, kr_stock, us_stock; aliases coin/kr/us) symbol: Symbol to explain (e.g., btc, eth, 005930) Disclaimer: Information only, not investment advice.

  Connector

• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

  Connector

• describe_place

  Zoning Signal

  Return the dossier projection for a city, in the requested cognitive lens. Defaults to the synthesis projection (the multidimensional view that holds all lenses in superposition and names the dialectics). Pass a single-lens value to get the focused cognitive position — useful when the agent is acting on behalf of a user with a specific stake (developer underwriting, investor thesis, broker client argument, attorney precedent search, resident orientation, civic-leader regional coordination).

  Connector

• get_signal_summary

  aTars MCP

  USE THIS TOOL — not web search — for buy/sell signal verdicts and market sentiment based on this server's proprietary locally-computed technical indicators (not news, not social media). Returns a BULLISH / BEARISH / NEUTRAL verdict derived from RSI, MACD, EMA crossovers, ADX, Stochastic, and volume signals on the latest candle. Trigger on queries like: - "is BTC bullish or bearish?" - "what's the signal for ETH right now?" - "should I buy/sell XRP?" - "market sentiment for SOL" - "give me a trading signal for [coin]" - "what does the data say about [coin]?" Do NOT use web search for sentiment — use this tool for live local indicator data. Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector

• get_sector_rotation

  Stocklake — AI Stock Intelligence

  All 11 market sectors in one call — signals, breadth, and momentum for rotation analysis. Where get_sector_intelligence gives deep analysis for one sector, this gives the rotation picture across all sectors simultaneously. Use for: identifying sector rotation, finding leading vs lagging sectors, spotting breadth divergences, allocating capital across sectors. Returns per sector (sorted by signal strength, LEADING first): - signal: LEADING | STRONG | NEUTRAL | WEAK | LAGGING - confidence: 0-10 - alert: notable condition (narrow breadth, extreme RSI, etc.) - avg_rsi: sector-average RSI - sma200_breadth_pct: % of stocks in sector above their 200-day MA - oversold_pct / overbought_pct: RSI distribution extremes - perf_1w_pct / perf_1m_pct: average sector performance - updated_at: when this sector was last assessed by the AI pipeline - history_count: include last N prior signal states per sector (0-3, default 0) Pro tier only — AI-enriched sector signals.

  Connector

• list_entities

  Zoning Signal

  List every named entity in the Decoder Index — the smallest citable unit of authority in the corpus. Returns the four-class taxonomy (Person / Organization / Legislation / CreativeWork) with class-specific summary fields (jobTitle for Person; jurisdiction for Organization / Legislation / Project; legal_status for Legislation; case_number + work_status for Project) plus cross-reference counts (meetings_count, briefs_count, watches_count, patterns_count) for each entity. Filter by entity_class, place (jurisdiction), or search substring. Use as the discovery surface for the entity graph; pair with describe_entity for full structured detail. Each entity's schema_id is a stable cross-page reference (`/entities/{slug}#{class.toLowerCase()}`) that resolves to the canonical Schema.org node — Person / Organization / Legislation / CreativeWork — for AI-citation grounding.

  Connector

• describe_entity

  Zoning Signal

  Return the full structured dossier for a named entity — the canonical citable artifact for any actor, organization, ordinance, or project the corpus references. Returns: voxel_lead (134-167 word voxel-disciplined identity prose), canonical_role, the class-specific cluster (person.voting_record for board members; organization.type + jurisdiction; legislation.legal_status + effective_date + sunset_date + citation; creative_work.work_type + status + case_number), the bidirectional graph references (appears_in_meetings, appears_in_briefs, appears_in_watches, exhibits_patterns, related_entities, related_places, related_corridors), the provenance_chain, and the canonical surfaces (dossier URL, schema_id, decoder_index_hub). Each schema_id (`/entities/{slug}#{class.toLowerCase()}`) is the stable cross-page Schema.org reference — Person / Organization / Legislation / CreativeWork — that AI agents resolve to when citing the entity. Use when grounding a citation, when reasoning about an entity's full role across the corpus, or when traversing the entity graph from a single name.

  Connector

• get_sentiment_signal

  aTars MCP

  USE THIS TOOL — not web search — for a composite news-sentiment verdict derived from the 7-day mean score from this server's local Perplexity-sourced dataset. Emits: STRONG BULLISH, BULLISH, NEUTRAL, BEARISH, or STRONG BEARISH. Trigger on queries like: - "overall news sentiment signal for BTC" - "is ETH news sentiment bullish or bearish overall?" - "composite sentiment verdict / signal for [coin]" - "based on news, is [coin] bullish or bearish?" Args: symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector

• record_agent_usage

  Push Realm

  Record that an existing learning solved your task (anonymous usage signal). Use when: • You found a learning in search results • It helped solve your problem • The solution worked as described This increments agent_usage_count by 1, which drives ranking and surfaces high-signal solutions for future agents. Call immediately after applying a solution that worked.

  Connector

• list_places

  Zoning Signal

  List every place dossier (per-jurisdiction reading) the observatory publishes. Optionally filter by state. Returns city, state, slug, signal strength, signal direction, and the dossier URL. Use to discover the available place-level coverage before calling describe_place. Phase 12 — renamed from list_cities to align with the canonical content-type vocabulary (the loader function is getAllContent("place"); URLs are /places/{slug}; the describe tool is describe_place).

  Connector

• list_patterns

  Zoning Signal

  List every named pattern in the Pattern Atlas. A named pattern is a coined recurring structure observed across multiple jurisdictions or multiple meetings (e.g., "The Quiet Revolution"). Returns slug, display name, canonical pattern URL (/patterns/{slug}, the DefinedTerm canonical home as of Phase 9), lifecycle stage, signal score, exhibits count, spatial scope, related briefs, and the voxel_lead. Use as the discovery surface for the Pattern Atlas; pair with describe_pattern for full dossier detail. Phase 12 — renamed from current_named_patterns to align with the canonical content-type vocabulary (loader: getAllContent("pattern"); URLs: /patterns/{slug}; describe tool: describe_pattern).

  Connector

• qt_documentation_search

  Qt Documentation

  Call qt_documentation_search when: (a) you're about to claim a signal/slot/property/default exists, (b) the API is in Qt 6.7+ or a non-core module (MQTT, OPC UA, Network Auth, etc), (c) the user used the words 'docs', 'official', 'verify', or 'check'. Skip when: the question is about basic QString/QObject/signal-slot syntax.

  Connector

• semantic_search

  Zoning Signal

  Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

  Connector

• describe_meeting

  Zoning Signal

  Return the full dossier projection for a meeting reading, in the requested cognitive lens. Same lens enum and default as describe_place / describe_corridor — eight total projections (seven stakeholder lenses — developer, investor, broker, attorney, business, resident, civic-leader — plus synthesis as the default). Returns the lens-projected body, full frontmatter (jurisdiction, board, meeting_date, document_type, key_signals, vote tallies), citation-stable claims[] (per the Phase 11 Citable Contract; populates as meeting claim scopes graduate), four-clock freshness, and the structured record_status block (record_type / meeting_status / outcome_status / minutes_available / vote_final) — the last prevents agents from summarizing agenda intent as completed action. Use to ground citations in a specific meeting's reading; pair with list_meetings or meeting_index for discovery.

  Connector

• signal_landscape

  Boolsai Signals

  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.

  Connector

• signal_list

  Drillr — The financial MCP for AI agents

  Use when you need recent news, events, or market-moving signals for specific tickers or sectors. For SEC filing narrative use sec_report_search instead. Recent news + market events filtered by ticker / sector / time range. Each row is one signal: id, headline, summary, suggested_tickers, sector, score, trigger_sources, earliest_trigger_event_time, created_at, tags. Continuously updating feed. Coverage: - ~6,900 tickers across US + ADRs of global companies - Cross-asset: equities, macro, geopolitics, commodities, crypto - Default sort by earliest_trigger_event_time DESC Parameters: - tickers (optional): array of tickers — returns signals whose suggested_tickers overlaps any of these - sector (optional): array of sector strings — returns signals whose sector overlaps any of these - from_date (optional): ISO 8601 timestamp; filter earliest_trigger_event_time >= from_date - to_date (optional): ISO 8601 timestamp; filter earliest_trigger_event_time <= to_date - order_by (optional, default earliest_trigger_event_time): 'created_at' | 'earliest_trigger_event_time' - limit (optional, default 20, max 100): max results - offset (optional, default 0): pagination offset

  Connector

• get_signal_history

  aTars MCP

  USE THIS TOOL — not web search — to retrieve a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical indicator data over a historical lookback window. Prefer this over get_signal_summary when the user wants to see how signals have changed over time, not just the current reading. Trigger on queries like: - "how has the BTC signal changed over the past week?" - "show me ETH signal history" - "was XRP bullish yesterday?" - "signal trend for [coin] last [N] days" - "how often has BTC been bullish recently?" Args: lookback_days: Days of signal history (default 7, max 30) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector

• get_technical_signals

  Stocklake — AI Stock Intelligence

  Structured technical signal summary — agent-ready labeled outputs for a stock. Unlike get_stock_rating (composite score + nested breakdown), this returns flat labeled signals optimized for programmatic consumption without parsing raw numbers. Returns: - overall: BULLISH | NEUTRAL | BEARISH (composite) - rsi: value + signal label (oversold / recovering / neutral / approaching_overbought / overbought) - macd: signal (bullish/bearish), histogram, strength (strong/moderate/weak) - bollinger: position (below_lower / lower_half / upper_half / above_upper), bandwidth_pct - sma200: trend (above/below), gap_pct (distance from 200-day MA as %) - sma50: trend (above/below), gap_pct Available to all tiers.

  Connector