tickerdb/tickerdb-mcp

Pre-computed stock market data for AI agents. TickerDB returns indicators like trend_direction, support_level, and analyst_consensus as named states — plus what *changed* and what *usually happens next*.
10,000+ US stocks, ETFs, and crypto pairs · 182 indicators across trend, momentum, volatility, volume, patterns, support/resistance, fundamentals, and sector context · 7 years of history · tickerdb.com
Tools
| Tool | Description |
|---|---|
| get_summary | Technical + fundamental snapshot for a ticker. Historical lookups, state transition history, and what usually happens after |
| get_ohlcv | Daily or weekly EOD candles for returns, charts, and backtests |
| get_search | Screen assets by categorical state or rank by fields like market_cap or pe_ratio |
| get_schema | Discover all 182 fields and their valid band values |
| get_watchlist | Full analytical summary for every ticker on your saved watchlist |
| get_watchlist_changes | What changed on your watchlist — day-over-day or week-over-week |
| add_to_watchlist | Add tickers to your watchlist |
| remove_from_watchlist | Remove tickers from your watchlist |
| get_account | Account details, plan tier, and usage |
All tools are available on every tier (Free, Plus, Pro). Tiers differ by credit limits, history depth, number of filters, and watchlist size. See tickerdb.com/pricing.
Quick start
Connect TickerDB to Claude, ChatGPT, or another MCP client (see Setup below), then try:
> **"Show me oversold large-cap stocks near support"**
The agent calls get_search with filters for momentum_rsi_zone = oversold and market_cap_tier in [large, mega], then follows up with get_summary on individual results. No raw number crunching — the agent reads categorical states and reasons over them directly.
> **"What usually happens when AAPL goes oversold?"**
get_summary with field=momentum_rsi_zone, band=oversold, stats=true returns aggregate aftermath distributions: how the stock performed 5, 10, 20, 50, and 100 days after each oversold entry over 7 years of history.
> **"What changed on my watchlist?"**
get_watchlist_changes returns only the field-level state transitions since the last pipeline run — band entries, exits, and shifts — so the agent reports what moved without pulling full summaries for every ticker.
Why not just pass raw OHLCV?
A model can compute RSI from raw bars. But ask "Does AAPL look bullish?" with raw OHLCV and it burns its context on arithmetic — computing indicators one by one — instead of doing what you actually asked: noticing that RSI just hit oversold while institutions are accumulating, that the pullback is sharp but the 200-day uptrend is intact, that insiders have been selling all quarter. That's the analysis. Raw bars bury it under computation.
With TickerDB, the model sees "oversold", "accumulation", "strong_uptrend" and connects them immediately.
**State transitions** go further. "What happened the last time BTC was this oversold?" means computing RSI across 7 years of daily bars, finding every oversold entry, and calculating what happened after each one. With TickerDB it's one call: get_summary with field=momentum_rsi_zone, band=oversold, stats=true.