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TickerDB

by com.tickerdb

get_summary

Read-only

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, including aftermath fields on paid tiers, weekly trend_stage analysis, pattern setup states such as pattern_bull_flag and pattern_ascending_triangle, MA signal fields, trend_ma_crossover_event, MA distance lookbacks such as trend_distance_ma40, and stock-only fundamentals_free_cash_flow events. Add stats=true in event mode to return aggregate event-band and aftermath distributions instead of raw rows. Results can include freshness via as_of_date, same-candle OHLCV, market_cap, market_cap_tier, trend, momentum (including divergence_detected, divergence_type, stochastic_zone), volatility (including squeeze_active, squeeze_days), volume (including climax_detected, climax_type), patterns, support/resistance, levels (paid tiers), sector_context (rsi_zone, trend, agreement, asset_vs_sector_rsi), and stock-only fundamentals such as raw pe_ratio (latest ratio on or before the snapshot date; negative values preserved and unavailable values null), free_cash_flow, growth_zone, earnings_proximity, earnings_proximity_basis, analyst_consensus, valuation_percentile, and nested insider_activity when available. Summary keeps sibling _meta objects off by default; set meta=true or request explicit *_meta fields when paid-tier stability metadata is needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoRange end date (YYYY-MM-DD). Use with start for historical series.
bandNoFilter events to a specific band value (e.g. deep_oversold, strong_uptrend, stage_2_growth). For MA distance event fields such as trend_distance_ma40, grouped aliases above and below are also supported. Only used with field.
dateNoHistorical date (YYYY-MM-DD) for a point-in-time snapshot. Requires Plus or Pro plan. Omit for latest.
metaNoSnapshot and history modes only. Add true to include sibling _meta / status_meta stability objects across the response. Explicit *_meta field paths in fields still work without this flag.
afterNoReturn events after this date (YYYY-MM-DD). Only used with field.
fieldNoBand field name for event queries (e.g. momentum_rsi_zone, extremes_condition, trend_direction, trend_stage, pattern_bull_flag, pattern_ascending_triangle, pattern_rising_wedge, trend_ma8_slope through trend_ma200_slope, trend_ma_crossover_event, trend_distance_ma40, fundamentals_valuation_zone, fundamentals_free_cash_flow, insider_zone, sector_rsi_zone, momentum_divergence_detected, fundamentals_analyst_consensus). When provided, returns band transition history instead of a snapshot.
limitNoFor event mode: max results (1-50), returned newest-first by default. For sample=even date ranges: requested sampled rows, capped by plan (Free 3, Plus 10, Pro 50).
startNoRange start date (YYYY-MM-DD). Use with end for historical series.
statsNoEvent mode only. Add true to return aggregate stats instead of raw event rows.
beforeNoReturn events before this date (YYYY-MM-DD). Only used with field.
fieldsNoOptional summary fields to return. Identity fields such as market_cap and market_cap_tier are always kept. Pass sections like ohlcv, trend, momentum, volatility, volume, patterns, extremes, support_level, resistance_level, fundamentals, sector_context, or levels (paid tiers). Or pass dotted paths like ohlcv.close, trend.direction, trend.stage, trend.ma_slopes.ma_8, trend.ma_slopes.ma_20, trend.ma_slopes.ma_40, trend.ma_slopes.ma_50, trend.ma_slopes.ma_100, trend.ma_slopes.ma_200, trend.moving_average_values.ma_8, trend.ma_crossover_event, trend.direction_meta, trend.distance_from_ma_band.ma_40, trend.volume_confirmation, momentum.rsi_zone, momentum.stochastic_zone, momentum.xtrm_score, momentum.divergence_detected, momentum.divergence_type, momentum.macd_state, patterns.bull_flag, patterns.bull_flag_breakout, patterns.bear_flag, patterns.bear_flag_breakdown, patterns.ascending_triangle, patterns.rising_wedge, volatility.squeeze_active, volatility.squeeze_days, volatility.regime_trend, volume.climax_detected, volume.climax_type, volume.accumulation_state, volume.price_direction_on_volume, support_level.level_price, support_level.status_meta, resistance_level.level_price, sector_context.rsi_zone, sector_context.trend, sector_context.agreement, sector_context.asset_vs_sector_rsi, sector_context.asset_vs_sector_trend, sector_context.oversold_count, sector_context.valuation_zone, fundamentals.pe_ratio, fundamentals.valuation_zone, fundamentals.growth_zone, fundamentals.free_cash_flow, fundamentals.earnings_proximity, fundamentals.earnings_proximity_basis, fundamentals.last_earnings_surprise, fundamentals.analyst_consensus, fundamentals.analyst_consensus_direction, fundamentals.valuation_percentile, fundamentals.pe_vs_historical_zone, fundamentals.pe_vs_sector_zone, fundamentals.insider_activity, fundamentals.insider_activity.zone, fundamentals.insider_activity.net_direction, levels, levels.support_levels, levels.resistance_levels. trend.stage is populated on weekly snapshots when stage evidence is sufficient. Event field names should prefer full schema names such as momentum_rsi_zone, extremes_condition, trend_stage, pattern_bull_flag, pattern_ascending_triangle, pattern_rising_wedge, trend_ma8_slope through trend_ma200_slope, trend_ma_crossover_event, trend_distance_ma40, fundamentals_valuation_zone, fundamentals_free_cash_flow, insider_zone, sector_rsi_zone, momentum_divergence_detected, and fundamentals_analyst_consensus.
sampleNoDate range mode only. Use 'even' to evenly distribute snapshots across the full start/end range.
tickerYesTicker symbol, e.g. AAPL, BTCUSD, SPY
timeframeNoAnalysis timeframe. Default: daily
context_bandNoOnly return events where the context ticker was in this band (e.g. downtrend). For MA distance context fields, grouped aliases above and below are also supported. Must be provided with context_ticker and context_field.
context_fieldNoBand field to check on the context ticker (e.g. trend_direction, trend_stage, or trend_distance_ma40). Must be provided with context_ticker and context_band.
context_tickerNoCross-asset correlation: a second ticker to filter against (e.g. SPY). Requires context_field and context_band. Plus/Pro only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoThe TickerDB API response payload for this tool call.

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: paid tier restrictions, meta objects being off by default, stats mode returning aggregates instead of rows, and the ability to request explicit *_meta fields. This goes well beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph with a lot of details. It is front-loaded with the main purpose, but the sheer length and stream-of-consciousness listing of fields and modes make it harder to scan. While the complexity of the tool justifies some length, better structuring (bullets or sections) would improve conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (17 parameters, 4 modes, paid tiers, nested objects), this description is exhaustive. It covers mode selection, field filtering, event semantics, stats behavior, meta flags, paid tier limitations, and even notes about fundamentals and insider_activity. The presence of an output schema means return values don't need explanation, and the description does not neglect any major aspect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema. The description adds some contextual semantics (e.g., 'Add stats=true in event mode' and 'meta=true or request explicit *_meta fields'), but these largely echo the schema descriptions. The description does help by grouping parameters into the four modes, but it does not significantly expand parameter meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Get pre-computed market intelligence for a specific stock, crypto, or ETF ticker.' It clearly distinguishes this tool from siblings like get_ohlcv (raw price data) and get_search (search functionality) by focusing on pre-computed summary intelligence. The four modes are explicitly listed, making the tool's scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use each mode (e.g., 'Snapshot (default) for the latest categorical state', 'Historical snapshot by date'), and explains conditional parameters like stats and meta. However, it does not explicitly state when NOT to use this tool or name alternative tools, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: add/remove watchlist manage saved tickers, get_watchlist analyzes all saved tickers, get_summary analyzes a single ticker, get_search finds assets by filters, get_ohlcv retrieves raw price data, get_schema discovers available fields, and get_account provides account limits. There is no meaningful overlap or ambiguity.

Naming Consistency4/5

Most tools follow a consistent 'get_' + noun pattern (get_account, get_ohlcv, get_schema, get_search, get_summary, get_watchlist). The watchlist mutation tools use 'add_to_watchlist' and 'remove_from_watchlist' instead of a simpler 'add_watchlist'/'remove_watchlist', which is a minor deviation but still predictable and logical.

Tool Count5/5

With 8 tools, this is a well-scoped set that covers market data retrieval, analysis, search, schema discovery, account management, and watchlist lifecycles. Each tool earns its place without unnecessary redundancy or bloat.

Completeness4/5

The tool set covers the core workflows: watchlist CRUD (add/remove/list), single-ticker analysis (get_summary), multi-ticker watchlist analysis (get_watchlist), raw historical data (get_ohlcv), search/discovery (get_search), field introspection (get_schema), and account management (get_account). Minor gaps include no batch historical data endpoint for multiple tickers and no update operation for watchlist entries, but these are not critical for the stated purpose.

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