Stocklake — AI Stock Intelligence

Get Stock Price History

get_stock_history

Read-onlyIdempotent

Daily OHLCV price history for a stock.

days: number of trading days to return (default 90, max 365)
Returns: { symbol, days_requested, days_returned, count, history[] } — days_returned/count can be less than days_requested if less history exists
Per bar: date, open, high, low, close, volume

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Number of trading days of daily OHLCV history to return (default 90, max 365). days_returned/count in the response can be less than this if less history exists.
`symbol`	Yes	Stock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

The description no annotations are present, so it carries the transparency burden. It discloses the default and maximum `days`, that `days_returned` may be lower than requested, and the exact per-bar fields. It does not explicitly state no-side-effect behavior, but the get/returns language plus the clarity of the operation make this a strong, non-hidden contract.

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 description is compact and organized as a short first line for the tool's purpose plus concise bullets for parameter and return semantics. Every sentence carries meaningful information and no filler or redundant material exists.

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?

The description, combined with the complete input schema and returned-val format, leaves no essential invocation behavioral detail unresolved: argument defaults, maximums, partial-history case, and bar fields are all present. This is fully sufficient for an agent to call the tool correctly.

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%, including `days`' default/max and `symbol`' ticker-plus-company-name behavior, so the added description value is limited. The description does clarify the effect of `days` beyond a single schema phrase and defines the return shape, but this goes beyond parameter semantics and the description says the schema already says.

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 states a specific verb-resource pairing: it returns daily OHLCV price history for a stock. This makes the tool's function obvious and distinguishes it from earnings, indicators, news, and screener siblings that provide different data types.

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?

It clearly establishes when this tool should be used: when a caller needs daily stock price bars rather than news or fundamentals. It does not explicitly name alternative tools or exclusion conditions, but the clear data scope provides enough contextual guidance for an agent to select it appropriately.

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.

Resources

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