valuein

Valuein Audit-grade SEC fundamentals & smart-money data for AI agents > Make investment calls your agent can defend — 30 years of point-in-time SEC…

От сообществаРаботаетБез входаГлобальныйБесплатноТолько чтение

Что умеет

  • Search Companies: 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 membe
  • Get Company Fundamentals: Retrieve standardized SEC EDGAR fundamental financial metrics for a US public company. Returns revenue, gross profit, operating income, net income, EPS (diluted), total asset
  • Get Valuation Metrics: Get comprehensive valuation and profitability metrics for a US public company. Returns per-period data combining computed ratios (gross_margin, operating_margin, net_margin, ROE

Какие данные видит

Нужен ли аккаунт

Не нужен: сервер работает без входа

Valuein

Audit-grade SEC fundamentals & smart-money data for AI agents

Make investment calls your agent can defend — 30 years of point-in-time SEC fundamentals and institutional smart-money flows, every number traceable to the filing it came from.

Valuein gives any MCP-compatible agent — Claude, Cursor, ChatGPT, Codex — the data a research analyst needs to reach a conclusion and defend it. One endpoint, two datasets, zero hand-waving.

What your agent pulls

Standardized fundamentals

  • 19,000+ active and delisted US companies, back to 1993
  • Income statement, balance sheet, cash flow, ratios, and valuation
  • Built-in reverse-DCF and forensic-accounting checks

Institutional smart-money signals

  • Insider buying & selling (Forms 3 / 4 / 5 / 144)
  • 13F manager portfolios
  • Blockholder moves
  • Net-flow direction

Why the numbers hold up

  • Point-in-time correct — zero look-ahead bias in your backtests
  • Survivorship-bias free — delisted names stay in the record, so history isn't quietly rewritten
  • One-click lineage — every figure traces straight back to its source SEC filing

The payoff: an answer that survives compliance review instead of needing re-verification.

One install, three ways in

The MCP server is the distribution channel. Install once, and the same token unlocks:

  1. The MCP server — for your agent
  2. The Python SDK — for your notebooks and pipelines
  3. The bulk Parquet API — for everything at scale

…all at your tier.

Free tier. No credit card.

Список инструментов сервера (60)

Технические названия из tools/list. Нужны только разработчикам.

search_companiesSearch 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.
get_company_fundamentalsRetrieve standardized SEC EDGAR fundamental financial metrics for a US public company. Returns revenue, gross profit, operating income, net income, EPS (diluted), total assets, total liabilities, stockholders' equity, cash & equivalents, total debt, operating cash flow, and capital expenditures for one or more fiscal periods. Data sourced from 10-K (annual) and 10-Q (quarterly) filings. Point-in-time: no look-ahead bias — pass `as_of_date` (YYYY-MM-DD) to reconstruct exactly the information set known on that date. This returns the raw as-reported line items ONLY. Do NOT derive metrics from them yourself — a hand-computed figure carries no fact_id and cannot be verified against a filing. Every derived metric is already served pre-computed WITH provenance: free cash flow, FCF margin, margins, ROE/ROA/ROIC, leverage and the price multiples come from `get_valuation_metrics`; the full ratio table (incl. per-share, owner-earnings, growth) from `get_financial_ratios`; intrinsic value from `compute_dcf`. If one of those is gated on your plan, say so and offer the upgrade — never substitute your own arithmetic.
get_valuation_metricsGet comprehensive valuation and profitability metrics for a US public company. Returns per-period data combining computed ratios (gross_margin, operating_margin, net_margin, ROE, ROA, ROIC, debt_to_equity, FCF, FCF margin), price-derived valuation_multiples (current_price, market_cap, pe_ratio, pb_ratio, ev_ebitda, dividend_yield), and optional pre-computed DCF model inputs (WACC, fcf_base_per_share, stage1_growth_rate, terminal_growth_rate, dcf_value_per_share, ddm_value_per_share). Profitability/cash-flow/leverage fields come from fact.parquet (PIT-safe via accepted_at). valuation_multiples are LIVE (schema 2.18.0): they come from ratio.parquet's `valuation` category + stock_price.parquet period-end close (per-period current_price for every fiscal year), derived from EOD prices period-end-aligned. Each multiple is a `{value, unit}` pair (unit varies: x / USD / percent); a null value carries a `null_reasons[field]` PRICE_NOT_AVAILABLE code (no period-end-aligned close). DCF/DDM fields come from valuation.parquet (pipeline-computed, recomputed each run — NOT strictly PIT-safe) and are commonly null (newer tickers, transition periods, or before the valuation pipeline runs). Each null carries a `null_reasons[field]` code — ALWAYS check it before assuming zero (null != 0). For strict-PIT DCF, use the SDK or compute from `get_company_fundamentals`. Use this *instead of* `get_financial_ratios` when DCF/intrinsic value or price multiples matter; use `get_financial_ratios` when you only need the raw ratio table. Available on all plans.
get_financial_ratiosGet pipeline-computed financial ratios from ratio.parquet. Served categories: profitability (margins, ROE, ROA, ROIC), liquidity (current ratio, quick ratio), leverage (D/E, interest coverage, net debt/EBITDA), efficiency (asset turnover, inventory days), per_share (EPS, BVPS, FCF/share), owner_earnings (Buffett FCF, owner yield), valuation (pe_ratio, pb_ratio, ev_ebitda, market_cap, dividend_yield), and the pipeline-emitted forensic, growth, and rank (cross-sectional *_sector_pctile) categories. NOT every category exists for every ticker — omit `categories` to get whatever this ticker has, or read `available_categories` in the CATEGORY_NOT_AVAILABLE envelope. valuation is LIVE (schema 2.18.0): price-derived multiples from EOD prices period-end-aligned — pipeline-derived, NOT strictly PIT (no accepted_at column on these rows). Includes TTM rows alongside annual; each row's `is_calendar_aligned` is TRUE only when period_end sits on the fiscal-year boundary (±7 days) — filter to TRUE when joining ratios to fact-table fundamentals on (entity, fiscal_year). For historical cuts use `as_of_date` (PIT by accepted_at when present, else by period_end — see the param). Use this *instead of* `get_valuation_metrics` when you only need ratios (no DCF wiring); use `get_valuation_metrics` when you also need DCF/DDM. Each ratio is a `{value, unit, category, reason}` entry with a response-level `lineage` (DerivedLineage) pointing to `get_company_fundamentals` / `verify_fact_lineage` for filing-level provenance; a null value carries a `reason` (e.g. INPUT_MISSING) so missing is never a real zero. Available on all plans.
get_sec_filing_linksGet direct links to original SEC EDGAR filings for any US public company. Returns four per-filing deep links: `sec_url` (the EDGAR filing-index page listing every document), `viewer_url` (the cgi-bin Financial-Report viewer for the specific accession), `inline_viewer_url` (the SEC Inline-XBRL viewer opened on the rendered primary document — the strongest provenance link, `null` when the filing is not Inline-XBRL), and `document_url` (a direct link to the rendered primary document itself — opens the actual filing, never the index page, `null` only when primary_document is unknown). Prefer `inline_viewer_url ?? document_url ?? viewer_url ?? sec_url`. Supported form_types (enum): 10-K, 10-Q, 8-K, 20-F, 40-F, 10-K/A, 10-Q/A, 20-F/A, 40-F/A. Other forms (6-K, DEF 14A, Form 4, 13F) are NOT yet exposed by this tool — use `describe_schema` to confirm the parquet has them, then read raw via the SDK. 8-K item codes are filterable via `event_types` (e.g. ['2.02'] for earnings, ['1.01'] for material agreements, ['5.02'] for officer changes). PIT-safe — filings are filtered by accepted_at, never by report_date alone. Use this *instead of* `verify_fact_lineage` when you want a list of filings; use `verify_fact_lineage` when you want one specific fact-to-filing trace. Available on all plans.
get_capital_allocation_profileGet a multi-year capital allocation breakdown for a US public company. Shows how management deploys cash across all six categories — capex, R&D, M&A, dividends, buybacks, and debt — plus pre-computed deployment ratios (% of operating cash flow) and over-distribution flags. Use this tool when the user asks: how does a company allocate capital, what's the buyback-vs-dividend mix, is the company over-distributing, is growth funded by R&D or M&A, what's the cash-return-ratio trend, or any 'where does the money go' question — including owner-earnings (Buffett-style) and reinvestment-rate (Damodaran-style) analysis. Data sourced from annual 10-K filings; PIT-safe via as_of_date. R&D is included as a deployment category (the primary growth-reinvestment vehicle for knowledge-economy firms), but since it's already deducted before operating cash flow, `rd_pct_ocf` is INFORMATIONAL and `total_deployment_pct_ocf` EXCLUDES R&D to preserve the cash-flow identity (OCF = capex + M&A + dividends + buybacks + debt repayment + Δcash). The `flags` object carries pre-computed booleans: `buybacks_exceed_fcf`, `total_returns_exceed_fcf` (buybacks + dividends > FCF), and `debt_funded_distribution` (over-distribution funded by leverage vs cash). Available on all plans.
get_peer_comparablesGet ratio-based peer comparison for a company and its closest competitors. Peers are selected by matching 2-digit SIC industry code. Returns pipeline-computed ratios from up to 10 peers alongside the subject company for direct benchmarking. Ratio categories: profitability, liquidity, leverage, efficiency, per_share, owner_earnings, valuation. TTM (trailing twelve months) ratios are used when available for the most current view. Use as_of_date to compare peers at a specific historical date. PIT semantics for the figure leg are data-driven: when the ratio data carries an SEC accepted_at timestamp, as_of_date filters point-in-time by accepted_at (zero look-ahead, _meta.pit_safe=true); when it does not (today's data), the cut is by ratio.period_end (_meta.pit_safe=false). NOTE: peer SELECTION still uses CURRENT S&P 500 membership as a size/relevance ranking proxy regardless of as_of_date (W3-G2). Available on every plan — sample returns the subset covered by the sample bucket.
get_pit_universeUse 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.
get_compute_ready_streamReturns a short-lived (15-min) download URL for a bulk Parquet object that can be piped directly into Python/DuckDB/Polars for high-throughput computation that exceeds the MCP context window. The URL streams the object straight from Valuein storage and supports HTTP range reads, so `duckdb.read_parquet(url)` / `pl.read_parquet(url)` work without downloading the whole file first. Datasets: fact (per-entity partition — requires ticker), ratio (all computed ratios), valuation (DCF inputs), filing (SEC filing metadata), references (company universe), index_membership (historical index composition). Scoped to the caller's tier bucket; the link is signed and cannot be used to list the bucket or read other objects.
describe_schemaReturns the Parquet schema for all tables in the Valuein SEC data warehouse. Includes table descriptions, column names, types, primary keys, and foreign-key references. Use this tool to understand the data model before querying with other tools. No data reads required — schema is embedded in the manifest. Available on all plans.
verify_fact_lineageUse this tool when the user asks BOTH what a financial figure is AND which filing reported it — e.g. "What was Apple's most recently reported revenue, and which 10-Q filed it?" or "Show me the accession ID for Tesla's latest net income." Returns a single fact plus its complete filing provenance: entity, concept, period, value, accession ID, filing URL, and form type (10-K, 10-Q, etc.). Use this INSTEAD OF `search_companies` when the user already names a company and wants a financial figure with its source filing — `search_companies` only resolves identifiers and returns no financial data. Use this INSTEAD OF `get_company_fundamentals` when the user explicitly wants the filing/form type or the accession ID — `get_company_fundamentals` returns metrics across periods but omits filing provenance. Two lookup modes: (1) by fact_id (deterministic SHA-256 identity) or (2) by concept name plus a ticker (most recently reported fact). Optionally pin a point-in-time cutoff via as_of_date (YYYY-MM-DD) — returns the latest filing accepted by SEC on or before that date (no look-ahead); check `_meta.pit_safe`. DURATION: a single 10-K tags BOTH a 12-month figure and a 3-month Q4 stub at the same period_end; on a tie this returns the longer (headline) window, and every result carries `period_type` and `period_span_days` so a 3-month stub is never mistaken for the annual figure. Provide either fact_id or concept (required). Returns FACT_NOT_FOUND if no matching fact exists. Available on all plans.
compare_periodsCompare a company's core financial metrics across two fiscal periods side-by-side. Shows absolute and percentage changes with significance classification (minor < 5%, notable 5–15%, significant > 15%). The response includes a `material_changes` count: this is the number of metrics whose `significance` ∈ {notable, significant} (i.e. absolute percentage change > 5%). Use it as a quick scalar to triage filings — anything > ~3 typically signals a material event worth deeper review. Use period format: 'FY2024' for annual, 'Q1-2024' for quarterly. Pass `period_a` as the EARLIER period and `period_b` as the LATER one — if you invert them the server auto-swaps and sets `swapped: true` in the response so deltas always carry the correct sign (rather than silently flipping). Point-in-time safe via as_of_date. Available on all plans.
screen_universeRank companies by cross-sectional factor scores from factor_scores.parquet. Returns the underlying factors (roe, gross_margin, operating_margin, net_profit_margin, revenue_growth_yoy, fcf_to_assets, debt_to_equity, asset_turnover, current_ratio, piotroski_f_score) plus their percentile ranks (1.0 = best in universe, 0.0 = worst). `composite_rank` (the default sort) is a one-number multi-factor shortcut; sort by a specific *_rank column for a single factor. Two modes: full-universe (omit ticker) or single-entity (ticker set — spot-check ONE company's factor profile). Sector filter is SIC-derived (GICS-aligned, not licensed GICS — see `get_pit_universe`). Use this *instead of* `get_financial_ratios` when you want CROSS-SECTIONAL comparison (rank vs peers); use `get_financial_ratios` when you want one company's ratios over time. Supports survivorship-free POINT-IN-TIME screening via `as_of_date` (see the param). Full-universe screens omit rows that don't join to a company (null symbol); pass `exclude_outliers=true` to also drop shell-company rows with implausible factors. Available on every plan — sample returns the subset covered by the sample bucket.
get_earnings_signalsReported earnings results and a model-derived earnings-trend signal for a company, by fiscal period: actual reported EPS, a trailing-trend EPS estimate (`eps_trend_est`), the deviation of actual vs that trend (`eps_surprise_pct`), reported revenue, and year-over-year revenue growth. IMPORTANT: `eps_trend_est` is NOT Wall Street analyst consensus — Valuein is sourced purely from SEC EDGAR and carries no consensus feed. It is a deterministic estimate computed from the company's own prior reported EPS, so `eps_surprise_pct` measures how far the print landed from its own trailing trend, not whether it 'beat the Street'. Use it to track earnings/revenue trajectory and momentum, not to claim a consensus beat or miss. Point-in-time safe — pass as_of_date to filter by SEC acceptance (accepted_at) for look-ahead-free backtests. Available on all plans.
get_stock_priceEnd-of-day closing price for a company AS OF any calendar date. Pass `date` to get the close on that day; if the date falls on a weekend or market holiday, it resolves backward to the most recent prior trading day's close (the `price_date` field tells you which day was actually used, and `resolved_backward` flags when it stepped back). Omit `date` for the latest available close. Closes are RAW (not split/dividend-adjusted); `div_cash` and `split_factor` carry the corporate-action factors for query-time total-return adjustment. This is EOD market data (not a SEC filing fact), so it carries a price_date rather than a fact_id. Coverage follows your plan's tier slice: full = all companies & all history, pro = all companies & last 15 years, sp500 = S&P 500 only, sample = S&P 500 & last 5 years. Available on all plans.
get_price_historyDaily EOD bar series (OHLCV + adjusted_close) for a company over a date range. Returns up to 252 trading-day bars oldest-first. Each bar carries: open / high / low / close (raw, unadjusted), adjusted_close (vendor split/dividend-adjusted — retroactively restated on each corporate action; use for total-return backtests; NOT PIT-immutable), volume (shares traded), div_cash (ex-dividend cash per share on that date, 0 on non-dividend days), and split_factor (1.0 on non-split days). Omit start_date for the trailing year before end_date. Omit end_date for the latest available close. Coverage follows your plan's tier slice: full = all companies & all history, pro = all companies & last 15 years, sp500 = S&P 500 only, sample = S&P 500 & last 5 years. Available on all plans.
get_pit_valuation_ratiosReturns a full snapshot of valuation multiples for a company on a specific historical date — zero look-ahead bias (the 'Compustat + CRSP merge' pattern). The EOD close is sourced from stock_price_daily.parquet at `as_of_date` (or the nearest prior trading day), and all financial figures come from SEC filings with accepted_at ≤ as_of_date so no future information is used. TTM financials are computed by summing the four most recent standalone-quarter values (or using the most recent FY filing when no quarterly series is available). Returns: price snapshot (close, price_date, is_exact_date_match), TTM P&L (revenue, gross_profit, operating_income, EBITDA, net_income, OCF, CapEx, FCF), balance sheet snapshot (shares, cash, debt, book equity), derived market values (market_cap, enterprise_value), valuation multiples (P/E, P/S, P/B, EV/EBITDA, EV/Revenue, FCF yield %), and TTM margins (gross, operating, net). Use for: historical valuation screens, backtesting entry-point multiples, forensic audit of peak / trough valuations, comparing a company's current multiples to its own history. Coverage follows your plan tier: full = all companies & full history, pro = all companies & last 15 years, sp500 = S&P 500 only, sample = S&P 500 & last 5 years. Available on all plans.
list_restatementsList financial-statement restatements — facts a later SEC filing materially changed (>0.5% swing) from what was originally reported. Each event carries the as-reported value, the restated value, the signed delta, a severity bucket, the RAW XBRL tag both filings used (the diff is same-tag, so it is apples-to-apples and checkable), both filings' accession numbers for one-click lineage, an analyst-importance tier (1 headline / 2 statement line / 3 footnote), the fact's rank within the company's restatement history, and — crucially — HOW the company told the market (`disclosure_class`): `non_reliance` (it filed an 8-K Item 4.02 telling the SEC not to rely on its prior financials), `amended` (a 10-K/A or 10-Q/A), or `undisclosed` (the number changed inside a routine 10-Q/10-K — no amendment, no 4.02). About 94% of events are `undisclosed`: most numbers that change, change quietly. `undisclosed` is a statement about the FILING CHAIN, not about the filer's intent — adopting a new accounting standard (ASC 606, ASC 842) legitimately restates prior comparatives with nobody doing anything wrong. Do NOT describe these as fraud, concealment, or wrongdoing. Filter by ticker, sector, severity, minimum swing, importance, disclosure class, or filing date; sort by recency (default) or significance; paginate with the returned cursor. Public data — available on every tier. Provenance: derived from SEC EDGAR filings; verify any figure with verify_fact_lineage.
submit_feedbackFile product feedback to the Valuein team — a bug, feature request, experience note, or data-quality issue — directly from the agent surface. Available on EVERY tier including guest/sample (no token required), so an agent can report a rough edge in-band without the human leaving the conversation. Provide a `category` and a `message` (other fields optional — see params). Authenticated callers can pass an `idempotency_key` so a retried submission files exactly once (the same key from the same account); guest/sample callers are never deduplicated. Returns a friendly acknowledgment you can relay to the user. Do NOT use this to query data; it is a one-way report channel.
submit_artifact_feedbackFile EXPLICIT, structured feedback about a specific artifact you (or the model) produced — a chat message, a report, a thesis, a claim, a tool call, or the schema. Use this (not `submit_feedback`) when you can name WHAT was judged and HOW: pass `target_type` + `target_id` + a `sentiment` (positive/negative/correction), and optionally a structured `reason` (e.g. wrong_number, bad_citation, hallucinated_fact), the `request_id` of the turn, the disputed `fact_id`, and an `expected_value` (the value it SHOULD have been, in your words). Available on EVERY tier including guest/sample. This is a one-way intake channel — it records your assertion, it NEVER computes or validates a number, and `expected_value` is stored verbatim, never trusted as data. Retried submissions of the same judgement on the same `request_id` file exactly once. Returns the recorded feedback id.
get_insider_transactionsForm 3 / 4 / 5 / 144 line items for a US public company. Returns each transaction (or initial holding / proposed sale) with the insider's name, role, transaction code, share count, price, and notional. Filters by lookback window, transaction code (P=purchase, S=sale, A=grant, M=option exercise, F=tax withholding, etc.), insider role, and minimum share threshold. Institutional tier only — sample / sp500 / pro return ENTITLEMENT_DENIED with an upgrade link.
get_institutional_holdingsReturns top-N institutional holders of a US public company at a specific period_end (latest by default), with aggregate institutional shares, total market value, holder count, and HHI concentration (sum of squared share-of-total percentages). Sourced from Form 13F-HR via the by-issuer partition. Institutional tier only. 13F filings carry a ~45-day reporting lag — staleness_warning fires when latest data is older than 90 days.
get_manager_portfolioReturns a 13F filer's full portfolio at a specific period_end (latest by default), with QoQ deltas vs the prior quarter (new / increased / decreased / exited / unchanged). Specify the filer either by filer_cik (preferred) or filer_name (fuzzy match against entity.name; multiple matches raise an ambiguity error so you can disambiguate by CIK). Institutional tier only.
get_blockholdersReturns SC 13D / SC 13G blockholder disclosures (5%+ stakes) for a US public company. Each row carries percent_owned, sole/shared voting + dispositive split, schedule_type, and the first-class ``going_active`` flag — TRUE when the same filer flipped 13G → 13D within the lookback window (the single most actionable activist signal in this dataset). Use latest_only=true (default) to dedupe to the most recent filing per filer. Use collapse_groups=true to fold multi-person filings into one row. Institutional tier only.
get_insider_sentimentRole-weighted insider sentiment score on a fixed [-100, +100] scale for a single issuer over a lookback window. Role weights: CEO/CFO = 3.0 (via officer_title pattern), other NEO Officer = 2.0, 10%-Owner = 1.5, Director = 1.0. P = +1, S = -1; option exercises, grants, and tax withholdings are neutralised. Cluster flag = TRUE when ≥3 distinct insiders transacted within any 30-day window inside the lookback. Institutional tier only.
get_top_holdersClassification-aware UNION across insider transactions (latest post_transaction_shares per insider), 13F institutional holdings, and SC 13D / 13G blockholder filings for one issuer. Each row carries holder_class ∈ {insider, institutional, blockholder_13D, blockholder_13G}. Dedupes overlapping filers by precedence (13D > 13G > institutional > insider). One call, classified cap table — Bloomberg charges separately for INSIDER<GO>, OWNER<GO>, and HDS<GO>; this consolidates them.
get_smart_money_flowComposite flow score on [-100, +100] aggregating insider transactions, 13F institutional Δ-shares vs the prior quarter, and SC 13D/13G blockholder changes over a lookback window. Each component normalised independently, then combined with configurable weights (default: institutional 0.4, blockholder 0.4, insider 0.2). Returns per-component attribution so an agent can see WHY the score is what it is — not just the headline number. NOTE: the institutional component is a QoQ share-change signal computed over the top-5 13F filers on a MATCHED current-vs-prior basis (a filer only counts when its prior-quarter book is observable), NOT the issuer's complete institutional book — treat the score as a directional signal, not an exact flow. `coverage.coverage_confidence` (0–1) reports how much of that basis had a real prior quarter; when it is 0 the institutional component is forced to 0 so a 13F ingestion gap can never surface as a false max-conviction buy. See the `coverage` block for holder coverage + staleness. The score is a unitless composite, not a dollar figure. Institutional tier only.
save_thesisPersist a directional investment thesis (bull / bear / neutral) on a ticker. The thesis becomes part of the caller's private research diary; pair with `list_theses` + `score_thesis_outcome` to track conviction-vs-outcome over time. Pass `idempotency_key` for at-most-once semantics from a retrying agent. **Use this AFTER** the agent has finished its analysis, not before — the thesis records the conclusion, not the question. Pair with `source_report_id` to link the thesis back to a published report so the buyer's thesis-tracking carries provenance. Tier: all paid + free tiers (sample tier rejected — sample is guest access with no customerId binding). Per-tier cap on # of stored theses: sp500=100, pro=500, full=10,000.
list_thesesReturn the caller's saved theses, newest-first. Filters: ticker (exact), view, status. Cursor-based pagination — pass `next_cursor` from the previous response to fetch the next page. Sample tier rejected (no per-user state).
get_thesisFetch a single saved thesis by its id. Returns the full record including outcome (if scored). Returns NOT_FOUND if the id is unknown or belongs to another user. For the claims composing a thesis use list_claims_for_thesis; for an individual claim use get_claim. Tier: paid + free (sample rejected).
delete_thesisSoft-delete a saved thesis: status flips to `archived` (the row stays for audit / re-scoring). Idempotent — archiving an already-archived thesis succeeds. Hard-delete is not supported by design; future versions may expire archived theses after N years. This does not delete the claims linked to the thesis — use delete_claim for those. Tier: paid + free (sample rejected).
score_thesis_outcomeGrade a saved thesis against fundamental momentum since its creation. Pulls revenue / operating-margin / EPS / OCF deltas and aggregates into a score in [-1, +1]. Bull theses are graded by directional alignment, bear by inverse, neutral by closeness-to-flat. The grade is persisted back to the thesis row; re-call to refresh once new fundamentals land. **Note (PR 2)**: scoring is fundamental-only — does NOT yet include market-price returns. Phase 2 will mix in price data via a partner feed; the response shape is stable.
score_due_thesesFind every thesis past its horizon with no outcome yet, and grade each via `score_thesis_outcome`. Operates on the caller's own theses by default; can target any user when called with `customer_id` from a `full`-tier (admin) token. Returns a summary + per-thesis results. Idempotent — a re-call only re-grades anything not already graded.
run_workflowResolve a saved workflow by id and return a structured execution plan for a single ticker. Each plan entry names a real MCP tool or SOP plus its ticker-substituted arguments; the calling agent invokes them in order, applying any `skip_if` predicate against the previous step's output. **This tool does NOT execute the steps server-side.** It plans; the agent runs. Iterate through `plan[]` in order, call the named tool/SOP with `args`, accumulate outputs, and apply each step's `skip_if` (skip the step when the previous output's `path` equals `equals`). Workflows are private state owned by the calling user. Sample-tier callers are rejected. Pair with `list_workflows` (frontend) to discover available workflow_ids.
list_public_theses_by_userReturn the PUBLIC theses + reputation aggregate for a user identified by Stripe customer_id. Used by the /[handle] profile page to render an analyst's track record. Only entries with visibility='public' are surfaced — private theses never leak. Reputation is correct/(correct+wrong) over graded theses; null when n < 5 (sample too small). Sample tier rejected; sp500+ only.
publish_thesisMake a saved thesis discoverable by flipping its visibility: `public` (default) surfaces it on the author's /[handle] profile and counts toward their reputation aggregate; `unlisted` makes it reachable at a known direct link but keeps it off the profile. Use AFTER save_thesis to promote an existing thesis (save_thesis sets visibility only at creation). Idempotent. Pair with unpublish_thesis to revert to private. Tier: sp500+ (sample rejected).
unpublish_thesisRevert a published thesis (public or unlisted) back to `private` — removes it from the author's /[handle] profile and excludes it from the public reputation aggregate. The inverse of publish_thesis. Owner-only, idempotent. Tier: sp500+ (sample rejected).
save_claimPersist a single falsifiable, evidence-backed CLAIM — the atomic unit of the research graph. Use this for each discrete assertion an analysis produces (e.g. 'NVDA gross margin stays above 70% through FY2026'), then compose claims into a thesis with `link_claim_to_thesis`. Claims are scored independently of theses, so claim accuracy is tracked as its own track record. Pick `claim_type` by HOW it's judged, not what it's about: `assertion` = true now, checked against data; `prediction` = resolves at `horizon_days` via `verifiable_condition`; `judgment` = qualitative, not auto-scored. Use `tags` for the topic (financial, valuation, macro, …). Set `eval_mode: 'auto'` + a `verifiable_condition` for deterministic grading, else `'agent'`/`'manual'`. Tier: all paid + free tiers (sample rejected — guest has no customerId). Verifiable claims must cite evidence.
list_claimsList the caller's saved claims, most-recent-first, with AND-composed filters and cursor pagination. Filter by ticker, claim_type (assertion/prediction/judgment), tag, or lifecycle status (open/confirmed/refuted/expired/stale/needs_review). Archived claims are excluded unless include_archived is set. Tier: all paid + free tiers (sample rejected).
get_claimFetch a single claim by id, plus the ids of theses it supports/refutes and its full append-only score history. Use this to inspect a claim's evidence, current status, and how its outcome has evolved. Tier: all paid + free tiers (sample rejected).
delete_claimSoft-delete a claim by id. The row and its score history are preserved for audit (archived, not erased); the claim drops out of default list_claims results. Idempotent — deleting an already-archived claim succeeds. Tier: all paid + free tiers (sample rejected).
link_claim_to_thesisAttach a claim to a thesis with a role: 'supports' (the claim, if true, strengthens the thesis), 'refutes' (if true, weakens it — track disconfirming evidence first-class), or 'context' (relevant but not directional). Idempotent — re-linking updates the role. A claim can support one thesis and refute another. This composes theses from claims; it does NOT make the thesis score a function of claim scores (they're scored independently). Tier: paid + free (sample rejected).
unlink_claim_from_thesisRemove the link between a claim and a thesis. Idempotent — succeeds whether or not the link existed. The claim and thesis themselves are untouched. Tier: paid + free (sample rejected).
list_claims_for_thesisList the claims composing a thesis, each with its role (supports/refutes/context). This is how you read a thesis as the structured argument it is — its supporting and disconfirming claims with their current statuses. Archived claims are omitted. Tier: paid + free (sample rejected).
score_claimResolve a claim's outcome. By default auto-grades an `auto` claim by evaluating its verifiable_condition against SEC fundamentals (confirmed/refuted), or marks it `needs_review` when it can't be resolved deterministically (judgment, antecedent, or missing data). To record a human/agent judgment instead, pass `manual_status` (+ optional score/reason). Idempotent — re-scoring the same resolution is a no-op. Tier: sp500+ (sample rejected).
score_due_claimsFind every auto-gradable claim that is due (assertions in open/needs_review/stale; predictions whose horizon has passed) and resolve each against fundamentals. Operates on the caller's own claims by default; can target any user when called with `customer_id` from a `full`-tier (admin) token. Returns a summary + per-claim results. Idempotent — re-calling only re-resolves what changed.
list_public_claims_by_userReturn the PUBLIC claims + claim-accuracy reputation for a user identified by Stripe customer_id. Used by the /[handle] profile to render an analyst's claim-level track record — a separate signal from thesis-outcome accuracy. Only visibility='public' claims surface; private state never leaks. Accuracy is confirmed/(confirmed+refuted) over resolved claims; null when n < 5. Sample tier rejected; sp500+ only.
publish_claimMake a saved claim discoverable by flipping its visibility: `public` (default) surfaces it on the author's /[handle] profile and counts toward their claim-accuracy reputation; `unlisted` makes it reachable at a known direct link but keeps it off the profile. Use AFTER save_claim to promote an existing claim. Idempotent. Pair with unpublish_claim to revert to private. Tier: sp500+ (sample rejected).
unpublish_claimRevert a published claim (public or unlisted) back to `private` — removes it from the author's /[handle] profile and excludes it from the public claim-accuracy aggregate. The inverse of publish_claim. Owner-only, idempotent. Tier: sp500+ (sample rejected).
save_citation_overridePersist a correction of a citation value. The correction is keyed on the canonical `fact_id` (a stable hash of CIK + accession + concept + period) so it applies to every report that references that same fact — including agent-regenerated reports. Re-saving the same fact_id replaces the prior correction in place (no duplicate row). The `fact_id` is VERIFIED against live SEC data (scoped to `ticker`) before the correction is stored — a fact_id that doesn't resolve to a real fact is rejected with FACT_NOT_FOUND and nothing is persisted. You therefore must supply the `ticker` the fact belongs to. Use this when the user notices an inaccuracy in an AI-generated report and wants the fix to persist. Provide `notes` for the rationale (≤500 chars) and `source_report_id` for provenance. Tier caps: sp500=500, pro=5000, full=50000.
list_citation_overridesAuthor-only newest-first listing of the caller's citation corrections. Filterable by ticker (e.g. all AAPL corrections) or by a single fact_id (returns 0 or 1 row). Pair with `save_citation_override` and `delete_citation_override`. Sample tier rejected. Agent use: call with `ticker` to introspect what corrections the user has previously applied on that ticker — useful for system prompts that respect prior corrections during regeneration.
delete_citation_overrideRemove a user-authored citation correction by fact_id. Idempotent — deleting a missing override returns deleted=false without error. Once deleted, reports that previously rendered the corrected value revert to the canonical fact value on next regeneration. Tier: paid + free (sample rejected).
save_watchlistUpsert a named watchlist with a list of tickers. Replace semantics — the full ticker list is the source of truth for that name. Use this for both creation AND modification (delete + recreate is not required for edits). 500-ticker cap per list. Names are case-insensitive uniqueness.
list_watchlistsPaginated newest-first listing of the caller's watchlists (id, name, tickers, status, counts). Filter by `status` (active/archived/all). Returns metadata only — use get_watchlist for one list's full ticker set, or watchlist_diff for new filings across a list. Tier: sp500+ (sample rejected).
get_watchlistFetch a single watchlist (full ticker set + criteria) by its name, not an id (case-insensitive). NOT_FOUND if the name is unknown to this user. Tier: sp500+ (sample rejected).
delete_watchlistSoft-delete a watchlist by its name (not id): status flips to `archived` (still readable via list_watchlists status=all/archived). The name is freed for reuse by a new save_watchlist. Idempotent. Tier: sp500+ (sample rejected).
set_agent_memoryStore or update ONE durable memory entry (key → value) for this user so context survives across sessions — preferences, prior conclusions, working context. Replace semantics per key (reusing a key overwrites it). Do NOT store a number you would later cite as a fact: financial figures come from data tools and carry fact_ids; memory values are never treated as verified figures. Caps: 200 entries / 8000 chars per value. Tier: sp500+ (sample rejected).
get_agent_memoryRecall this user's durable memory. Omit `key` (or pass null) to read EVERYTHING you have remembered, newest-first — do this at the START of a task to re-ground yourself. Pass a specific `key` to fetch one entry. An absent key returns an empty list, never an error (absence is a first-class answer). Tier: sp500+ (sample rejected).
watchlist_diffReturn new SEC filings across the caller's watchlist tickers since a given date. Reads filing.parquet — does not call insider/ratio surfaces (use those tools separately if you need them). Concurrency-bounded; max 50 tickers per call.
create_alertPersist an alert and register it with the firing pipeline. Five condition shapes: * `filing_event` — fire when a ticker files a chosen form type (8-K, 10-K, etc.). * `ratio_threshold` — fire when a ticker's financial ratio crosses a threshold (e.g. interest_coverage < 1.5). * `watchlist_change` — fire on any filing on any ticker in a named watchlist. * `price_move` (Pro+) — fire when a ticker's close-to-close move over 1/5/21 trading days crosses a percent threshold in a given direction. * `fundamental_change` (Pro+) — fire when a standard_concept reports a brand-new period or gets restated. Delivery channels: `email` (transactional via Resend), `webhook` (HMAC-SHA256-signed POST), `slack` (hooks.slack.com incoming webhook), `dashboard` (in-app inbox), or `agent_run` (Pro+ — runs a standing agent team and delivers the finished artifact to your inbox). The cron evaluator runs every 5 minutes. Use `test_alert` to verify your channel is wired correctly before relying on the cron.
valuein: подключить к Claude, ChatGPT, Cursor · Connectors.fun