Daily OHLCV bars plus VWAP, range_pct and true_range_pct for one ticker over a date range. range_pct = (high - low) / open is a ready-made volatility read; true_range_pct also captures the overnight gap. Day-level aggregates — the cheapest way to cover long histories. $0.001/year.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | End date YYYY-MM-DD | |
| start | Yes | Start date YYYY-MM-DD | |
| ticker | Yes | Stock ticker (e.g. AAPL) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses daily aggregates, derived metrics (range_pct, true_range_pct capturing volatility), cost, and single ticker scope. Could add that it is read-only and returns a time series of bars, but current disclosure is solid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tightly written sentences with no fluff. Purpose is front-loaded, critical details (cost, derived metrics) follow efficiently. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 simple parameters and no output schema, the description provides sufficient context: it names the return fields and includes cost. Could explicitly mention it returns a list/array of bars, but the description is largely complete for typical agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for ticker, start, end. The description adds significant meaning: explains that start/end are dates, ticker is a stock ticker, and crucially describes the output (OHLCV plus VWAP and volatility metrics). This goes well beyond the schema's parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns daily OHLCV bars plus VWAP, range_pct, and true_range_pct for one ticker over a date range. It distinguishes from sibling tools like query_minute_bars (intraday) and get_pricing (likely raw pricing) by specifying 'day-level aggregates' and highlighting cost efficiency for long histories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for long-term historical analysis with 'cheapest way to cover long histories' and $0.001/year cost. It provides clear context but does not explicitly state when to use versus alternatives like get_bars or query_batch, nor list exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but there is some overlap among bar-related tools (get_bars, query_minute_bars, query_range, query_daily). Descriptions are detailed enough to differentiate, but an agent might need to carefully choose the right bar tool.
Naming follows a consistent verb_noun pattern (get_*, query_*, scan_*). The only minor deviation is list_tickers instead of get_tickers, but it's still predictable.
15 tools is well-scoped for a market data API covering prices, fundamentals, filings, insiders, and screening. Each tool serves a clear purpose without being excessive.
The tool set covers a comprehensive range of market data needs: intraday/daily bars, fundamentals, SEC filings, insider transactions, company profiles, and screening. Notable missing features like options data are beyond the stated scope.