TickerDB MCP Server

Which funds held a stock on one date (SEC Form N-PORT, point in time: each fund's newest portfolio filed by the date), largest positions first, plus the tracked indexes it belonged to (index_memberships) and totals over every holder (fund_count, fund_shares). Each fund's shares and value_usd are as of its own report_date, typically 2-5 months earlier. Costs 1 credit plus 1 per 100 funds beyond 100.

A fund's full portfolio as public on one date (SEC Form N-PORT), largest positions first: every line as filed (stocks, bonds, cash funds, derivatives) with ticker (TickerDB symbol on that date when the CUSIP maps to an asset we cover, else null), cusip, isin, balance, value_usd, weight_pct (percent of net assets), asset_category (EC = common stock). Point in time: filing.filed_on is when the portfolio became public and filing.report_date the quarter end it describes (typically 2-5 months before the date). Coverage starts with the fund's first N-PORT (late 2019). Costs 1 credit plus 1 per 100 lines beyond 100.

Get your account details including current plan tier, monthly credit limits, and current usage. Response includes tier, limits (monthly_requests, overage_enabled, search_results, history_days), and usage (monthly_requests_used, monthly_requests_remaining, credit_balance for pay-per-use accounts). Also returns scheduled_tier and scheduled_change_at if a plan change is pending.

Get field-level band changes of the newest published date for up to 50 tickers: day-over-day for daily, week-over-week for weekly. Each change is {field, from, to}; Plus and Pro also get stability, periods_in_current_state and recent flips. ticker_context gives each ticker's last_changed_date. Use it for 'what changed for AAPL, MSFT and NVDA today?' instead of pulling full summaries; use get_summary for one ticker's full state.

Find funds (ETFs and mutual funds) that publish holdings to the SEC on Form N-PORT, by name words or ticker, largest first. Each fund has fund_id (SEC series id), tickers, is_etf, its first and newest public portfolio dates. Then use get_fund_holdings for a portfolio, or held_by in get_universe / get_search. Names, tickers and sizes are current, not point in time. Costs 1 credit.

Search for assets matching filter criteria, including categorical states (e.g. oversold assets, strong uptrends, bull/bear flag setups, triangle or wedge setups, free-cash-flow surplus or burn, recent golden crosses, weekly stage 2 assets near the 40w MA with high volume, volatility squeeze active, volume climax detected, insider buying zone, sector-aligned breakouts, extreme fear, cheap PEG) or rankings by a field such as market_cap_usd on a historical date. Pass filters as a JSON-encoded array of {field, op, value} objects. Use get_schema to discover valid field names (prefer it over guessing). Bands end in a category word (_zone, _state, _direction, _trend, _tier), raw numbers in their unit (_pct, _ratio, _price, _usd, _count, _periods, _score). Examples: momentum_rsi_zone, momentum_rsi (0-100), trend_direction, trend_stage, trend_ma50_slope_zone, trend_distance_ma40_zone (raw trend_distance_ma40_pct), trend_ma_compression_zone, trend_ma_crossover_event, pattern_bull_flag_state, pattern_bull_flag_breakout, pattern_ascending_triangle_state, volume_ratio_zone, volatility_squeeze_active, range_position_zone, fundamentals_free_cash_flow_zone, fundamentals_revenue_zone, fundamentals_peg_ratio / fundamentals_peg_zone, fundamentals_earnings_proximity_zone, forward_outlook_earnings_zone, insider_zone, sector_stocks_oversold_count, fear_greed_score / fear_greed_zone (per asset), and GARP Core garp_core_score / garp_core_score_tier / garp_core_rank (GARP from valuation, business quality and resilience, consistent across all history, built for backtests). Names from before the 2026-10-31 renames (volume_ratio_band, trend_distance_ma50, pattern_bull_flag, fundamentals_revenue, range_position, sector_total_count) are still accepted; responses use the new names. MARKET CONDITIONS: market-wide market_* and economy-wide macro_* bands (e.g. market_regime_zone, market_breadth_ma200_zone, market_fear_greed_zone, macro_regime_zone, macro_yield_curve_zone, macro_yield_curve_zone_prev, macro_recession_risk_zone) can be mixed into filters: a market filter keeps only the dates where the condition held, matched on each ticker row's own date; putting a market field in fields attaches that date's value to every row. With start and end plus at least one market filter, every matching date in the range is screened (rows carry date), e.g. stocks oversold on the days breadth was washed out. Use get_market for the market rows themselves. Use fields to control returned columns and sort_by to rank results server-side.

An index's members on one date, point in time: the long stock positions of its tracking fund's newest SEC N-PORT portfolio FILED by that date (source.report_date is the portfolio's own quarter-end date, source.filed_on when it became public; a stock added to the index after report_date appears with the next filing). Each member has ticker (as of the date), name and weight_pct (percent of fund net assets). Ids from get_indexes. To screen members by fields, use get_universe or get_search with member_of instead. Costs 1 credit plus 1 per 100 members beyond 100.

List the indexes whose membership TickerDB tracks (sp500, sp400, sp600, sp100, nasdaq100, russell1000, russell2000), each with the fund it is read from (e.g. IVV for the S&P 500), its first covered date (late 2019) and today's member count. Membership is the SEC Form N-PORT holdings of a fully replicating fund, point in time by filing date: typically 2-5 months behind the index provider's own changes. Use the ids with get_index_members, or as member_of in get_universe / get_search. Costs 1 credit.

One insider's disclosed transactions (SEC Form 4), ordered by disclosure time: the event series of a copy/inverse insider model. Get the slug from get_insiders first. Two rules for backtests: act at disclosed_at (when it became public), never trade_date (up to lag_days earlier; using it is look-ahead); and only open-market trades are decisions: set open_market_only=true to drop grants, option exercises, tax withholding and gifts, and consider exclude_planned=true (pre-scheduled Rule 10b5-1 sales carry little signal; nearly all of Jeff Bezos's Amazon sales are planned). Each trade has ticker (as of disclosure), side, shares, price, value, shares_owned_after, planned, late and an sec_url. Join to get_ohlcv for prices.

Find insiders (people and organisations whose trades are disclosed in SEC Form 4: CEOs, officers, directors, 10%+ owners including funds) by name, ticker or type, or list them most active first. Step 1 of a copy/inverse insider model ('make a model that copies Bezos'): search the name, pick the right result from its companies, roles and activity, then call get_insider_trades with its slug. Search is loose: any order and case, word starts, initials, nicknames both ways (bill = william), typos, run-together or split names (jeffbezos, value act), legal suffixes ignored, and extra words tolerated (half the words must match; full matches rank first, matched_words says how many matched). Several people can share a name: check companies and roles. Curated aliases cover famous names filed differently (Jensen Huang files as 'Huang Jen Hsun'). Each result has lag_days_median/_p90 (days from trade to public disclosure) and late_filing_share.

Get the market level: regime bands for the whole US equity market (market_*) and the US economy (macro_*), one row per date, point in time. Use it for 'what regime is the market in?', 'is breadth washed out?', 'is the yield curve inverted?', 'is the economy in stagflation?', 'what is the recession risk?', 'when did the yield curve last un-invert?', 'list every market regime change in 2024'. Key bands: market_regime_zone (bull_broad, bull_narrow, correction, bear, recovery), market_breadth_ma200_zone (washed_out .. overextended), market_fear_greed_zone, market_valuation_zone, market_volatility_zone, market_risk_appetite_zone, market_earnings_season_zone, macro_regime_zone (goldilocks, reflation, stagflation, deflationary_slowdown), macro_recession_risk_zone, macro_yield_curve_zone (deeply_inverted, inverted, flat, normal, steep), macro_fed_policy_zone, macro_inflation_zone, macro_inflation_trend, macro_labor_market_zone, macro_sahm_rule_zone, macro_gdp_zone. Headline numbers: market_fear_greed_score, market_stocks_below_ma200_pct, market_aggregate_pe_ratio, macro_treasury_yield_2y_pct, macro_treasury_yield_10y_pct, macro_yield_curve_10y_minus_2y_pct, macro_fed_funds_rate_pct, macro_cpi_inflation_yoy_pct, macro_unemployment_rate_pct, macro_gdp_growth_annualized_pct. Every band carries change markers (_prev = label left on the row it changed, _periods = rows in the current label, 1 = changed today, _flips_recent_count, _stability); set changes=true to get only dates where a band changed, with from/to. macro_ values are as first published (never revised later) and low-frequency releases carry until the next one (null once stale). To find tickers under a market condition, put market_/macro_ filters into get_search instead (they match each ticker row's own date). get_schema lists every market field with level=market.

Get stored end-of-day OHLCV candles for a stock, ETF, or crypto ticker, daily or weekly. Use this for exact-return calculations, charts, and backtests after get_summary identifies a setup. Results are paginated; pass next_cursor back as cursor to continue. Prices are stored as traded; adjustment picks the basis: split (default), split_dividend (total return) or raw. Credit cost is 1 credit per 100 bars returned, rounded up, with a 1 credit minimum.

Get the schema of all available fields and their valid band values, with a plain-words description, unit and minimum plan for each. Every field has level: ticker (per-asset snapshot columns for get_search, get_summary) or market (market_* and macro_* regime bands, one row per date, for get_market and as get_search filters matched on the same date). Use this when the user asks 'what fields are available?', 'what bands does momentum_rsi_zone have?', 'what macro bands exist?', or when you need to validate field/band names before calling get_summary with event parameters, get_search with filters, or get_market. Field names follow <group>_<name>[_<unit>]; bands end in a category word (_zone, _trend, _state ...). The response also lists the naming rules and notes that pre-2026-10-31 names are still accepted.

When the data last changed (free, 0 credits): for daily and weekly snapshots and each asset class, the newest published date, whether it is preliminary or final (eod), its row count and when it was released; the market level's newest date; recent releases; and the release schedule. Call it to decide whether new data is out before re-pulling summaries, search results or a universe, instead of polling ticker tools.

Get pre-computed market intelligence for a specific stock, crypto, or ETF ticker. Supports 4 modes: (1) Snapshot (default) for the latest categorical state; (2) Historical snapshot by date; (3) Historical series with start and end dates; (4) Events by field and optional band (when a band was entered, what it left, how long the prior state lasted), for any tracked band: momentum_rsi_zone, trend_direction, trend_stage, trend_ma8_slope_zone through trend_ma200_slope_zone, trend_distance_ma40_zone, trend_ma_crossover_event, pattern_bull_flag_state, pattern_ascending_triangle_state, volume_ratio_zone, fear_greed_zone, fundamentals_free_cash_flow_zone, fundamentals_peg_zone, forward_outlook_earnings_zone, garp_core_score_tier and more (old names such as pattern_bull_flag or volume_ratio_band are still accepted). Add stats=true in event mode for aggregate counts by band. Results include as_of_date, same-candle OHLCV, market_cap_usd, market_cap_tier, performance_zone, trend (ma_slopes, distance_from_ma_zone, ma_compression_zone, volume_confirmation_state), momentum (raw rsi, stochastic_k, stochastic_d beside their zones), extremes (condition_rarity_percentile), volatility, volume (ratio_zone, price_on_volume_direction), patterns (*_state), support/resistance (distance_zone, volume_at_tests_zone), range_position_zone, breakout_type, fear_greed (score 0-100 and zone), levels (paid tiers), sector_context (stocks_oversold_count, stocks_count, asset_vs_sector_rsi_zone ...), and stock-only fundamentals (pe_ratio, pb_ratio, peg_ratio, peg_zone, reporting_currency, revenue_zone, profitability_zone, cash_flow_zone, balance_sheet_zone, free_cash_flow_zone, earnings_proximity_zone, analyst_consensus_zone, forward_outlook.*_zone, insider_activity) plus the garp block (Pro; GARP Core in garp.core: GARP from valuation, business quality and resilience, consistent across all history, built for backtests). Market-wide and macro regimes are not part of a ticker summary: use get_market. Summary keeps sibling _meta objects off by default; set meta=true or request explicit *_meta fields when paid-tier stability metadata is needed.

Get selected fields for EVERY asset on one date in a single call (about 11,000 rows), ordered by ticker: the input of a daily or weekly trading model run. Fields use the same names as get_search and are tier-gated the same way; at most 60 per call. Costs 5 credits per 10 fields. member_of / held_by restrict it to an index's or a fund's holdings on that date ("S&P 500 members with garp_core_score"). A row dated D reflects everything known by 04:00 UTC on D+1, so a backtest acts at the next session's open. For filtered or ranked subsets use get_search; for one ticker use get_summary.