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 provided, the description carries full burden. It discloses the data content, defines derived metrics (range_pct and true_range_pct), notes the one-ticker limitation, and states cost ($0.001/year) — valuable behavioral context beyond the schema.
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?
Four sentences, each earning its place: core purpose, formula clarifications, use-case/cost context. No redundant or vague language. Front-loaded with the essential information.
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 simple 3-parameter data-retrieval tool with no output schema, the description is complete enough: it specifies the data returned, the computed metrics, the single-ticker scope, and pricing. It lacks explicit error handling or response format, but the given context does not demand it.
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 description coverage is 100%, so the schema already explains ticker, start, and end fully. The description does not add meaning to the parameters themselves; it only describes output fields, which is not part of parameter semantics.
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 with additional VWAP and volatility metrics for one ticker over a date range. It effectively distinguishes from siblings by specifying 'one ticker' and 'Daily' vs minute/batch tools.
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?
It implies when to use this tool by noting 'Day-level aggregates — the cheapest way to cover long histories' and 'one ticker', contrasting with broader or more granular alternatives. However, it does not explicitly name sibling tools or exclusions, so slightly below the highest bar.
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.
Several tools overlap significantly: get_bars, query_minute_bars, query_range, and query_batch all provide intraday bar data, making it hard to distinguish when to use which. Additionally, get_brief and get_fundamentals overlap in fundamentals coverage. The descriptions do not clearly delineate boundaries between these tools.
The naming pattern is a mix of get_* and query_* prefixes, with list_ and scan_ as exceptions. While each individual name is readable, similar operations like get_bars and query_minute_bars use different verbs, making the overall convention inconsistent.
15 tools is a reasonable count for a comprehensive financial data server, covering market data, fundamentals, filings, insiders, and scanning. The count is not excessive, though the overlapping intraday bar tools suggest some redundancy that could be consolidated.
The tool surface is fairly complete for its domain, offering intraday and daily market data, fundamentals, SEC filings, insider transactions, company profiles, and market scanning. Minor gaps exist (e.g., no dedicated dividend/split tool), but these are not critical and can be worked around via existing tools like get_brief.