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Stocklake — AI Stock Intelligence

by dev.stocklake

Get Indicator History

get_indicator_history
Read-onlyIdempotent

Historical daily indicator snapshots for a stock — ideal for charting and trend detection. Returns up to days days of data (max 730, default 90) from the stock_indicator_snapshots collection which is populated daily by the indicator pipeline.

Each snapshot row contains:

  • recorded_at: ISO date string

  • price: closing price at snapshot time

  • rsi: RSI(14) value (0-100)

  • macd_histogram: MACD histogram value (positive = bullish momentum)

  • bb_pct: Bollinger Band % position (0 = at lower, 100 = at upper band)

  • sma20 / sma200: 20-day and 200-day simple moving averages

  • williams_r: Williams %R (0 to -100; >-20 overbought, <-80 oversold)

  • ultimate_osc: Ultimate Oscillator (0-100; >70 overbought, <30 oversold)

  • vix_fix_value: Williams VIX Fix synthetic fear gauge (higher = more fear)

  • williams_ad_trend: Accumulation/Distribution trend (rising/falling/flat)

  • td_signal: DeMark TD Sequential signal (BUY_SETUP/SELL_SETUP/BUY_COUNTDOWN/SELL_COUNTDOWN/null)

  • td_phase: DeMark phase (setup_active/setup_complete/countdown_active/countdown_done/null)

  • analyst_rating: analyst consensus (buy/outperform/hold/underperform/sell or null)

  • analyst_target: mean analyst price target or null

Returns {} if fewer than 3 snapshots found. Pro tier only. For informational purposes only. Not financial advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days of historical daily indicator snapshots to return (max 730, default 90). Returns {} if fewer than 3 snapshots are found in the window.
symbolYesStock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, and the description substantially adds context: maximum/default days, empty-result behavior, that data comes from a daily pipeline, and Pro-tier restriction. Each row's field semantics are explained, which goes well beyond what annotations alone provide.

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

Conciseness5/5

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

The structure is logical and front-loaded: purpose first, data-window and sourcing second, then a row-level column reference. The bulk of the length is a compact field list with inline interpretation, which is genuinely useful for an agent interpreting output rather than filler.

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 tool of this complexity with 15+ indicator fields, the description covers purpose, data source, window semantics, empty-result behavior, output field meanings, Pro-tier restriction, and a disclaimer. Combined with a 100%-coverage schema and output schema, nothing an agent needs to call or interpret this tool is missing.

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%, but the description adds value by explaining the meaning and behavior around the `days` parameter (default, max, and the empty-result rule) and by clarifying that `symbol` also accepts a company-name fallback. The field-level breakdown is output semantics, yet it helps an agent reason about which parameters affect the result.

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 and resource ('Historical daily indicator snapshots for a stock') and an explicit use case ('ideal for charting and trend detection'). It clearly separates this tool from price-history siblings by emphasizing indicator snapshots, and lists a rich set of indicators so an agent knows exactly what to expect.

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 states when it is appropriate ('Historical daily indicator snapshots for a stock — ideal for charting and trend detection') and sets expectations around the date window and data population. It does not explicitly name which sibling tool to use for simple price history, so it does not fully reach a 5 on exclusion guidance.

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

A4.3/5.0
Disambiguation4/5

Most tools carve out clearly distinct jobs: per-stock quotes, batch quotes, earnings calendars, market pulse, news, signals, screener, watchlist. A few pairs—notably get_stock vs get_stocks and get_market_assessment vs get_market_pulse—are close enough that an agent would need to read descriptions carefully to pick the right one.

Naming Consistency5/5

All tool names follow a uniform get_<snake_case_noun> pattern with no mixed conventions. The singular/plural pair get_stock and get_stocks is a deliberate batch-version distinction rather than an inconsistency, and compounds like earnings_intelligence vs market_assessment remain predictable.

Tool Count4/5

With 17 tools, the server is slightly over the ideal 3-15 range, but the count is well-justified by the breadth of the stock-intelligence domain: per-stock, batch, historical, market-level, earnings, insider, news, signals, screeners, and watchlists all have a dedicated entry point. It is a larger surface area than ideal, but none of the tools feel redundant or purely decorative.

Completeness4/5

The tool surface gives agents full read-oriented workflows: single-stock data, batch lookups, historical prices, indicator history, news, research bundles, insider activity, market health, earnings, signals, movers, sector intelligence, and screening all exist. The main gap is discovery—there is no symbol/name search tool beyond the screener category filters, and historical earnings-surprise data is absent—but agents can work around both via existing tools.

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