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tickerdb

TickerAPI

Official

get_search

Read-only

Search stocks, crypto, and ETFs using filter criteria for technical patterns, momentum zones, fundamentals, and rankings. Filter by fields like oversold RSI, bull flags, golden crosses, or sort by market cap.

Instructions

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) or rankings by a field such as market_cap on a historical date. Pass filters as a JSON-encoded array of {field, op, value} objects. Use get_schema to discover valid field names; fields use clean flat names for raw values such as pe_ratio, ma8, and ma200, and full expanded names for semantic fields such as momentum_rsi_zone, pattern_bull_flag, pattern_bull_flag_breakout, pattern_bear_flag_breakdown, pattern_ascending_triangle, pattern_rising_wedge, trend_ma_crossover_event, trend_distance_ma40, trend_stage, fundamentals_free_cash_flow, insider_zone, sector_agreement, volatility_squeeze_active, volume_climax_detected, fundamentals_analyst_consensus, and fundamentals_earnings_proximity, fundamentals_earnings_proximity_basis. Use fields to control returned columns and sort_by to rank results server-side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoHistorical snapshot date (YYYY-MM-DD). Omit for latest per asset class.
limitNoMax results to return. Tier-gated: Starter 25, Plus 100, Pro 500. Default: 20
fieldsNoJSON-encoded array of column names to return. Example: ["ticker", "sector", "market_cap", "pe_ratio", "trend_stage", "ma40", "trend_ma50_slope", "trend_ma_crossover_event", "trend_distance_ma40", "pattern_bull_flag", "pattern_bull_flag_breakout", "pattern_bear_flag_breakdown", "pattern_ascending_triangle", "fundamentals_free_cash_flow", "volume_ratio_band", "insider_zone", "sector_agreement", "volatility_squeeze_active", "volume_climax_detected", "fundamentals_analyst_consensus", "fundamentals_earnings_proximity", "fundamentals_earnings_proximity_basis"]. Omit to get a default core subset: ticker, asset_class, sector, market_cap, market_cap_tier, performance, trend_direction, trend_ma20_slope, trend_ma_compression_band, trend_ma_crossover_event, momentum_rsi_zone, extremes_condition, extremes_condition_rarity, volatility_regime, volume_ratio_band, pattern_bull_flag, pattern_bull_flag_breakout, pattern_bear_flag, pattern_bear_flag_breakdown, pattern_ascending_triangle, pattern_descending_triangle, pattern_symmetrical_triangle, pattern_rising_wedge, pattern_falling_wedge, fundamentals_valuation_zone, range_position. Request fundamentals_free_cash_flow explicitly when you need the stock-only free cash flow burn/surplus band. Request ma8 through ma200 for raw MA values and trend_ma8_slope through trend_ma200_slope for the full MA slope set. Use ["*"] for all fields. Specify fields to reduce token usage. trend_stage is weekly-only and should be requested with timeframe=weekly. Insider fields (insider_zone, insider_net_direction) and sector context fields (sector_rsi_zone, sector_trend, sector_agreement) are available on paid tiers.
filtersYesJSON-encoded filter array. Each filter: {"field": "column_name", "op": "eq|neq|in|gt|gte|lt|lte", "value": "..."}. Example: [{"field": "momentum_rsi_zone", "op": "in", "value": ["oversold", "deep_oversold"]}, {"field": "sector", "op": "eq", "value": "Technology"}]
sort_byNoColumn name to sort results by (e.g. "market_cap", "pe_ratio", "extremes_condition_percentile", "fundamentals_valuation_percentile", "volume_percentile", "sector_oversold_count", "sector_breakout_count"). Must be a valid field name from the schema. Server-side sorting avoids pulling extra fields for client-side ranking.
timeframeNoAnalysis timeframe. Default: daily
sort_directionNoSort direction. Default: desc. Use 'asc' for lowest-first (e.g. cheapest valuation percentile).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoThe TickerDB API response payload for this tool call.
Behavior4/5

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

Annotations already indicate read-only and non-destructive behavior, so the description doesn't need to repeat that. It adds valuable context about filter encoding, field naming conventions, and server-side sorting, which helps agents understand the tool's expected inputs and behavior. No contradictions with annotations.

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

Conciseness4/5

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

The description is a single dense paragraph but front-loaded with the core purpose. Every sentence provides useful information about filter types, field naming, and usage. While somewhat long, it avoids fluff and structures the information logically.

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

Completeness4/5

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

Given the tool's complexity (7 parameters, output schema present, annotations available), the description covers key aspects: search use cases, filter format, field discoverability, and column/sort control. It leaves mention of pagination and defaults to the schema, which is acceptable given the output schema exists.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning by explaining the JSON-encoded filter format, listing example semantic fields, and clarifying the distinction between raw and expanded field names, which goes beyond the schema's property descriptions.

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 clearly states it searches for assets matching filter criteria, including categorical states and rankings by fields. It distinguishes itself from sibling tools like get_summary and get_ohlcv by focusing on filtered asset search, making the purpose 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 gives clear context for when to use the tool (searching assets with filters) and provides a cross-reference to get_schema for field discovery. It does not explicitly state when not to use it versus alternatives, but the guidance on filters and fields implies its role as the primary search tool.

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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