"author:BAWES-Universe" matching MCP tools:

• deltasignal_alpha_sweep

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for opportunity and alpha screening across the current DeltaSignal issuer universe. It server-enforces the alpha-sweep call plan: readiness, alpha_opportunities with limit 15, and daily_changes; alpha_opportunities defaults to operating-company issuers. Parameters: optional output_mode=compact only; do not pass limit, offset, ticker, source_date, or issuer filters because this preset owns exact arguments internally. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, never calls issuer-level tools, and preserves partial results if one internal call fails. Use it when the user asks for alpha opportunities, opportunity sweep, clean alpha board, or names worth follow-up research; treat the result as a screen requiring issuer drilldown.

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• imf_query_dataset

  imf-mcp-server

  Query an IMF SDMX dataflow by dimension key over a time range. Returns observations with time_period, value, unit, scale, and status attributes. Requires imf_get_database first to obtain the correct key_format and valid dimension codes. Country codes are ISO 3-letter (USA, GBR, DEU — not US, GB, DE). Key format: dot-separated codes in DSD keyPosition order (e.g. USA.NGDP_RPCH.A for WEO). Use + to specify multiple codes per position (e.g. USA+GBR.NGDP_RPCH.A). Codelists from imf_get_database enumerate the code universe, not actual coverage — valid codes can still return no_data if the combination has no series. start_period and end_period must be valid period strings (YYYY, YYYY-QN, or YYYY-MM) with start_period no later than end_period; malformed or reversed ranges are rejected. Large analytical result sets (multi-country, long time range) spill to DataCanvas; imf_dataframe_query provides SQL analysis of spilled results.

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• get_pit_universe

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Use this tool to answer questions about historical index membership — e.g. "Was Company X in the S&P 500 on date Y?" or "Which companies were in the Russell 2000 on 2010-01-01?" Use this INSTEAD OF `search_companies` when the question involves a specific historical date or whether a company was an index member in the past — `search_companies` only returns current membership and cannot answer historical questions. Returns a survivorship-free universe valid on a given as_of_date (only companies that existed and were members on that exact date — no hindsight). Supports SP500, RUSSELL1000, RUSSELL2000, RUSSELL3000 via index_membership.parquet (accurate join/leave dates, [) interval semantics). To check one company, pass its ticker + the target date: present = was a member, absent = was not. Returns per company: CIK, ticker, name, sector, industry, SIC code, and per-row confidence (high/medium/low). `_meta.pit_safe` is true only when every matched row is high-confidence — treat low-confidence rows with caution. `sector` is SIC-derived (GICS-aligned, not licensed GICS) — a screening bucket, not an authoritative label. Use as the first step of a quantitative backtest before `get_compute_ready_stream`. Returns an empty array (with error detail) if the date is out of range or has no coverage. Available on every plan — sample returns the subset covered by the sample bucket.

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• deltasignal_risk_distribution

  DeltaSignal ATLAS-7

  Use this read-only tool to summarize the active crypto public company universe by ATLAS-7 risk tier. It returns risk-tier buckets such as HIGH, MODERATE, LOW, and UNCLASSIFIED with issuer counts and percentages. Parameters: none; call it exactly as-is when the user asks for market-wide risk mix or high-level distribution. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not write external systems or access user accounts. Use it for market-wide context before issuer drilldown; use top_stressed to name the issuers in the high-risk bucket and use issuer tools for company-level analysis.

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• deltasignal_alpha_opportunities_audit

  DeltaSignal ATLAS-7

  Use this read-only diagnostic tool to explain why the alpha-opportunity board includes, excludes, or demotes rows. It returns issuer-type, identity, quality-gate, and raw-alpha-versus-board-rank summaries from the same scoring universe used by deltasignal_alpha_opportunities. Parameters: limit is 1-100 for bounded samples; source_date replays a known YYYY-MM-DD slice; issuer_type narrows the audit to operating_company, etf_trust, fund_vehicle, foreign_issuer, unresolved_identifier, or all; include_rows=true attaches full publishable audit rows and should be used only for explicit debugging. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state. Use it after deltasignal_alpha_opportunities or deltasignal_alpha_sweep when the user asks why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize.

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• secedgar_fetch_frames

  secedgar-mcp-server

  Fetch SEC XBRL frames for one concept × one period across all reporting companies. Inline response returns the top N ranked companies; the full frames response (all reporters) is materialized as df_<id> when a canvas is available, queryable via secedgar_dataframe_query. Accepts friendly names like "revenue" or "assets" (discover via secedgar_search_concepts) or raw XBRL tags. One call hits one XBRL tag — when a friendly name maps to multiple same-meaning tags, the response's `unqueried_tags` lists the others; call again per tag and UNION/COALESCE in SQL with an analysis-specific priority (e.g. SalesRevenueGoodsNet is goods-only). The response's `related_tags` separately flags alternate-DEFINITION tags a meaningful share of filers use as their primary line (e.g. cash incl. restricted cash, equity incl. noncontrolling interest) — a whole-universe screen on the base tag silently omits those filers; query them separately, but do not blindly union (the semantics differ). Response includes `value_distribution` and `period_end_range` to flag XBRL scale-factor anomalies and fiscal-year mixing.

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• query_robotics_adoption_v1

  osint

  Query the verified share of US manufacturing plants USING industrial robots — plus workers exposed and robotics capex — from the Census Industrial Robotic Equipment product (the first official federal robotics-adoption statistics). Use this for "are factories actually adopting robots" questions — the INSTALLED-BASE reading the import data cannot see. Serves the percent of plants with robots and the percent of employees at plants with robots (both published as FRACTIONS of 1: 0.121 = 12.1%), Census's demeaned variants, and capital expenditures for robotic equipment ($1000) — by manufacturing industry (`naics_code`, 2/3-digit), by `state`, and by `plant_size` band. Filter by `edition` ("asm_2018_2021" = the ASM annual series; "ec_2022" = the 2022 Economic Census), `table` (the workbook sheet — exactly one of: "Percent of... NAICS", "Percent of... Geo", "Percent of... Geo demean", "Robot adopters vs not", "Robot adoption and plant size", "CapEx... NAICS", "CapEx... Geo", "CapEx and plant size"), `data_year` (2018-2022), `naics_code`, `state`, `plant_size`, or `geo_area_name` ("United States" for the national row). Group by any of `edition`, `table`, `naics_code`, `naics_title`, `state`, `plant_size`, `data_year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"table": "Percent of... Geo", "data_year": 2022, "group_by": ["state"], "order_by": "avg_pct_plants_with_robots", "top_n": 10}` for the most-automated states; `{"table": "Percent of... NAICS", "edition": "asm_2018_2021", "naics_code": "336", "group_by": ["data_year"]}` for transportation-equipment adoption over the ASM years. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites its exact workbook cell-group, re-verifiable via get_source_evidence_v1. THE ENGRAVED BOUNDARY: the two editions are NEVER spliced into one trend — the 2022 Economic Census reaches the small-plant universe the ASM sample does not (US plants-with-robots: 12.1% ASM-2021 vs 6.4% EC-2022 — a COVERAGE change, not a decline; every cross-edition scope carries an edition_scope note). Percents are INTENSIVE shares: avg/min/max over a scope, never summed. Capex sums UNDERSHOOT below the published totals wherever suppression bites (the published "United States" / "31-33" rows are the totals). Suppressed cells (D/S/A, decoded by the file's own footnotes) are null values with their verbatim letter — never zero; (s)-flagged estimates (standard error > 40%) carry their flag. Manufacturing plants only; adoption SHARES and capex, never robot counts (no official count of installed robots exists — the import unit-count series is query_robotics_trade_v1); an EXPERIMENTAL Census product (its own label); no edition after 2022 exists.

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• portfolio_stress_test

  crashtestyourstrategy

  Stress a multi-asset portfolio across cross-asset regimes (baseline / risk_off_crisis / rate_shock). Provide `holdings` as a list of {asset, weight}; weights are normalised. Returns, per regime: portfolio return, worst-episode drawdown, a per-leg decomposition, and a cross_asset_finding (diversification_intact / hedge_holds / hedge_breaks / shared_drawdown) describing how the holdings behaved TOGETHER. The joint correlation structure (incl. the bond hedge that can break under rate shocks) is baked into a pre-computed substrate, so Tier-1 is instant over a fixed universe (read portfolio://universe). Optional `costs` ({rebalance: none|daily|monthly|quarterly|band, annual_costs: {asset: fraction}, transaction_cost_bps}) adds a cost_impact block: frictionless vs the stated rebalancing policy + costs via a path-loop engine with real unit accounting, paired on identical paths. The substrate is a fixed 4-asset universe (SPY, TLT, GOLD, BTC; read portfolio://universe). For ANY other ticker or a custom multi-asset book, use build_portfolio in assess mode (portfolios={name:{ticker:weight}}), which calibrates and stresses an arbitrary universe live. Descriptive, not advisory.

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• get_pool

  GammaRips Options Intelligence

  The GammaRips candidate pool for a scan date. One tool, four `view`s: * view="enriched" (DEFAULT) — the curated AI-enriched pool: news, technicals, catalyst, a delta-targeted recommended contract, and the 60-day momentum feature `mom_60`. Enrichment gate: overnight_score>=4 AND directional UOA>$500K, edge-ranked to the top ~50 BULLISH names. This is the daily candidate set your agent reasons over to its OWN contract (see get_playbook("run-your-own-tournament")). Served from a leakage-safe view (forward-outcome columns physically stripped); `summary=True` gives ~21 decision columns, `fields=[...]` a strict projection, `summary=False` full rows, `offset` pages. * view="raw" — the wide pre-curation overnight scan (where unusual options activity concentrated across the whole universe, BEFORE curation). Honors `direction`, `min_score`, `ticker`, `limit`. * view="features" — point-in-time FEATURE VECTORS from the leakage-safe allowlist view `enriched_features_v1` (identity + features + cohort metadata only; no outcome/label/telemetry column can appear). The quantitative substrate for joining against query_outcomes. Lags the live pool by ~1-2 trading days. * view="preview" — a minimal public teaser (ticker, direction, score, headline, directional UOA) for the most recent scan; no contract specifics or thesis. TIER: view="preview" is FREE (no key). The enriched / raw / features views are the paid product — they require an active pro subscription key; an anon call to them returns `subscription_required` (get_pool(view='preview') is named as the free entry point). Liquidity caveat (all views): `recommended_oi`/`recommended_volume` are scan-time snapshots, not live values; `recommended_spread_pct` is permanently NULL on the current data plan — re-check with get_liquidity. Args: view: "enriched" (default) | "raw" | "features" | "preview". scan_date: YYYY-MM-DD (default: latest available scan for the view). direction: "bull"/"bear" prefix filter (enriched / raw). ticker: exact ticker filter (enriched / raw / features). min_score: overnight_score floor (raw view only; clamped 0-10). limit: max rows (enriched/raw clamp 1-50, features 1-100, preview 1-20). summary: enriched only — True=compact columns, False=full rows. fields: enriched only — explicit strict column projection. offset: enriched only — pagination offset.

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• search_companies

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Search for US public companies by name, ticker symbol, CIK (SEC identifier), or SIC industry code. Returns ticker, company name, sector, industry, exchange, and current S&P 500 membership status. Use this tool to resolve a company name to ticker/CIK before calling `get_company_fundamentals`, `get_valuation_metrics`, or other tools that require a ticker — they do not fuzzy-match company names. **Use this tool — NOT `get_pit_universe` — when the user asks about CURRENT S&P 500 members.** To list current S&P 500 members, call `search_companies({ is_sp500: true })` (the `is_sp500` filter is itself a valid search parameter, so no other input is required). This returns the live snapshot as of query time. Example: "List 5 current S&P 500 members" → call `search_companies({ is_sp500: true, limit: 5 })`. **Use `get_pit_universe` ONLY when the user explicitly needs a survivorship-free historical universe as of a specific past date** (e.g. "S&P 500 members as of March 2018"). If the user says "current," "today," "now," or gives no date, use `search_companies` instead. **Data details:** `sic_code` is the 4-digit SIC; `industry` is the human-readable label. `sector` is SIC-derived with GICS-style labels — NOT licensed GICS, so industrial conglomerates may map differently from official GICS (e.g. 3M → 'Health Care' by SIC vs Industrials by GICS). S&P 500 membership is sourced from index_membership.parquet (current SP500 = `index_name='SP500' AND removal_date IS NULL`). Available on all plans.

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• deltasignal_alpha_sweep

  DeltaSignal ATLAS-7

  Use this read-only composite workflow tool for opportunity and alpha screening across the current DeltaSignal issuer universe. It server-enforces the alpha-sweep call plan: readiness, alpha_opportunities with limit 15, and daily_changes; alpha_opportunities defaults to operating-company issuers. Parameters: optional output_mode=compact only; do not pass limit, offset, ticker, source_date, or issuer filters because this preset owns exact arguments internally. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, never calls issuer-level tools, and preserves partial results if one internal call fails. Use it when the user asks for alpha opportunities, opportunity sweep, clean alpha board, or names worth follow-up research; treat the result as a screen requiring issuer drilldown.

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• deltasignal_alpha_opportunities_audit

  DeltaSignal ATLAS-7

  Use this read-only diagnostic tool to explain why the alpha-opportunity board includes, excludes, or demotes rows. It returns issuer-type, identity, quality-gate, and raw-alpha-versus-board-rank summaries from the same scoring universe used by deltasignal_alpha_opportunities. Parameters: limit is 1-100 for bounded samples; source_date replays a known YYYY-MM-DD slice; issuer_type narrows the audit to operating_company, etf_trust, fund_vehicle, foreign_issuer, unresolved_identifier, or all; include_rows=true attaches full publishable audit rows and should be used only for explicit debugging. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state. Use it after deltasignal_alpha_opportunities or deltasignal_alpha_sweep when the user asks why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize.

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• get_earnings_calendar

  Stocklake — AI Stock Intelligence

  Upcoming earnings dates for stocks in the Stocklake universe. - days: look-ahead window in days (default 7, max 30) - Returns: { window_days, from_date, to_date, count, results[] } - Each result: symbol, name, sector, market_cap, price, rsi, earnings_date (ISO UTC), is_estimate, eps_trailing, eps_forward - Sorted by earnings_date ascending. - Dates sourced from market data — treat is_estimate=true dates as approximate. Available to all tiers.

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• get_news_feed

  Stocklake — AI Stock Intelligence

  Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by AI flag score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_flag_score: minimum AI flag score (default 8, min 5, max 10) 8 = notable · 9 = high-impact · 10 = exceptional - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• tickerbot_list_system_universes

  mcp-server

  List the built-in system universes (`top_10`, `top_100` — the most-actively-traded tickers by 30-day trailing dollar volume, rebalanced monthly). Available to every account regardless of plan. Use these slugs as `universe` in scans/signals or `universe_id` when subscribing.

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• create_portfolio_draft

  Bullrun

  Use when the user wants you to BUILD or PROPOSE a brand-new portfolio for them — e.g. "build me a portfolio", "put together a dividend portfolio", "draft a portfolio of AI stocks", "create a new portfolio for $10k". Generates a REVIEWABLE paper-portfolio draft for the signed-in Bullrun user from a natural-language brief (e.g. "a diversified European dividend portfolio"). Requires OAuth with the write:drafts scope and a Bullrun Pro account. This is DRAFT-ONLY and never changes any live position: the draft is saved to the user's account and appears in the Bullrun Portfolio tab under "Pending AI drafts", where the user reviews it and explicitly accepts it to create a new portfolio (or discards it). To suggest additions to an EXISTING portfolio instead, use create_position_draft. Tickers are chosen only from Bullrun's priced stock/ETF universe; pass instrumentUniverse for stocks only, ETFs only, or a mix. If the brief is vague, first ask ONE quick round of up to three multiple-choice questions (investing style, region focus, and size), each with a default the user can accept with "just pick for me", then build; skip any dimension the user already specified and do not interrogate across multiple turns.

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• run_backtest

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  A SMALL, BOUNDED, in-Worker sanity-check backtest — NOT a full-universe backtesting engine. Answers a quick question like 'does this factor actually work on these 5 names over the last year' inline, mid-conversation, without leaving MCP. Composes two existing tools (`get_pit_universe` + `get_pit_valuation_ratios`) across up to 10 tickers x 12 rebalance dates (120 cells): for each rebalance date, checks which requested tickers were in the survivorship-free PIT universe on that date (dropping — never erroring on — a ticker not yet listed or already delisted), then pulls each surviving ticker's point-in-time valuation multiples and computes the forward return to the NEXT rebalance date from the raw (unadjusted) close. Returns a flat {rebalance_date, ticker, factor_values, forward_return_pct} grid plus a small factor<->forward-return correlation per requested factor — a quick cross-sectional signal check, NOT a transaction-cost-aware portfolio simulation or a statistically validated backtest result. If the requested grid exceeds 120 cells, this tool does NOT silently truncate — it returns a `stream_fallback` response (signed Parquet download URLs, same shape as `get_compute_ready_stream`) and tells you to use those URLs. For a REAL full-universe, multi-date, survivorship-free backtest, use the Python SDK's AlphaEngine (`pip install valuein-sdk`) looped over `as_of` dates client-side — this tool is explicitly the small complement to that, not a replacement for it. Available on every plan; coverage follows your plan tier same as the two tools it composes.

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• screener

  Stock Market Data MCP

  Screen the tracked large-cap universe by market cap, trailing P/E, dividend yield, and sector — returns matching stocks ranked by market cap. A raw market-data filter; for a ranked, fundamentals-driven screen pair this with financial-signals-mcp's composite_value_score. PAID: $0.01 USDC per query after a daily free allowance. On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.

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• deltasignal_atlas7_companyfacts_history

  DeltaSignal ATLAS-7

  Use this read-only tool to retrieve compact SEC CompanyFacts/XBRL materialization rows for the crypto public-company universe or a specific ticker/CIK. It returns one compact row per issuer for a materialized companyfacts source_date, including tag counts, crypto/digital-asset flags, top tags, taxonomies, evidence hashes, and fact source pointers. Parameters: source_date replays a known YYYY-MM-DD materialization slice; ticker or cik optionally narrows to one issuer; limit defaults to 25 and is capped at 250; offset paginates the universe. Behavior: read-only and idempotent; it performs no writes, has no destructive side effects, and never returns the raw facts_payload, so use fact_source_pointer plus evidence_hash for drilldown/audit. Use this for Mirror Pulse or ATLAS-7 historical joins when you need the compact issuer-level CompanyFacts inventory for all 215 crypto companies.

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• deltasignal_atlas7_audit_status

  DeltaSignal ATLAS-7

  Use this read-only tool to check whether the Azure-native ATLAS-7 full-universe regression audit is healthy. It reads the latest audit summary artifact from Azure Blob and reports last successful run time, issuer count, operation count, failure counts, historical route status, composite route status, and artifact prefix. Parameters: none. Behavior: read-only and idempotent; it has no destructive side effects, does not run the audit, mutate data, or access raw issuer evidence. Use this before trusting historical ATLAS-7 surfaces in an agent workflow or when an operator asks whether the nightly 215-issuer audit is current.

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