Available date range and estimated bar count for a symbol/frequency.
Available on paid plans. Call before a server-side fetch so the requested start/end stay inside what the provider can deliver and the bar count stays inside the key's per-run limit.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| frequency | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full burden. It discloses that the tool is 'available on paid plans' and that it should be called before fetching. It implies the tool is a read-only metadata query with no side effects, which is sufficient behavioral context.
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?
The description is two sentences long, front-loaded with the tool's purpose, then the usage guideline. Every sentence provides unique information with no redundancy.
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?
The description covers the tool's purpose, when to use it, and key behavioral traits. However, with no parameter explanations and 0% schema coverage, it is incomplete for a tool requiring two untyped string parameters. The presence of an output schema mitigates the need to describe return values.
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 0%, yet the description does not explain the parameters beyond their names ('symbol', 'frequency'). It adds no details about valid values, formats, or constraints, which would be essential for correct invocation.
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 'available date range and estimated bar count for a symbol/frequency.' It specifies the exact resource (date range and bar count) and verb (get), and differentiates from sibling tools like get_price_history or get_quote by emphasizing its role as a pre-fetch check.
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 explicitly says 'Call before a server-side fetch' and explains the purpose: to ensure the requested start/end stay within provider's delivery and bar count within per-run limit. This gives clear when-to-use guidance, though it does not mention when not to use or alternative tools.
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.
Every tool targets a distinct operation or resource: backtesting, comparison, macro data, reference catalogs, etc. Even similar tools like run_backtest and compare_backtests are clearly differentiated by purpose and inputs.
Overall consistent verb_noun pattern in snake_case, with a few exceptions like engine_info (noun_noun) and export_backtest (verb_noun but less common verb). The pattern is predictable and aids agent selection.
20 tools is slightly above the ideal range but justified by the breadth of the platform (backtesting, data retrieval, reference, export). Each tool serves a clear purpose without redundancy.
Covers the full backtesting lifecycle: strategy validation, data sourcing, backtesting, comparison, export, and reference lookups. Minor gaps exist (e.g., no explicit strategy persistence), but the core workflow is complete.