FREE, no payment: real intraday OHLCV bars for AAPL on 2024-01-02, identical in shape to a paid query. Call this first to verify data quality before spending. Takes no arguments — fixed ticker and date.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses that the tool is free, uses real data, and is identical in shape to paid queries, setting appropriate expectations for a read-only verification tool.
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?
Two concise sentences with no fluff. Front-loaded with key attraction ('FREE, no payment') and purpose, then details. Every word 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?
Given the tool's simplicity (0 parameters, fixed data), the description fully covers purpose, usage, and behavioral context. No output schema needed for such a sample tool.
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?
The schema has no parameters, so description coverage is 100%. The description reinforces that it takes no arguments, which matches the schema, but adds no new semantic meaning beyond what's already clear.
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 FREE real intraday OHLCV bars for AAPL on a fixed date, and explicitly says 'Call this first to verify data quality before spending', distinguishing it from sibling tools like get_bars or query_minute_bars.
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 gives explicit guidance: 'Call this first to verify data quality before spending', implying use before paid queries, and notes it takes no arguments, so it's not for other tickers/dates.
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.