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Glama

TickerDB

by com.tickerdb

get_search

Read-only

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.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare safe read-only behavior. Description adds behavioral details beyond schema: field naming conventions (clean flat vs expanded names), default field subset behavior, tier-specific availability of insider/sector fields, and server-side sorting rationale. No contradictions; could further explain openWorldHint but not required.

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 long but front-loaded with purpose and organized into consequential guidance (filters, fields, sorting, caveats). It earns its length; however, the density may slow scanning, so slightly below a perfect conciseness score.

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?

For a complex search tool with seven params and an output schema, the description covers filter syntax, field discovery, default behavior, tier limits, and mode-specific constraints. It leaves no major functional gaps; output shape is handled by the schema.

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

Parameters5/5

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

Despite 100% schema coverage, description substantially enriches parameter understanding by explaining the raw vs semantic field name distinction, listing meaningful example filters, documenting default fields, and noting tier-gated availability. This goes far beyond the input schema's 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 opens with 'Search for assets matching filter criteria' and enumerates specific categorical states and ranking use cases. This clearly identifies the tool as an asset-screening/search function, distinct from sibling tools like get_ohlcv or get_summary.

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?

Provides explicit context: 'Use get_schema to discover valid field names', 'Pass filters as JSON-encoded array', and advises 'trend_stage is weekly-only and should be requested with timeframe=weekly'. However, it doesn't contrast with alternatives like get_summary or get_ohlcv for when not to use this tool, so not 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.

Resources