get_equity_curveBacktest performance of the quant strategy across your watchlist: Sharpe ratio, max drawdown, win rate and total return per symbol.
Available to every authenticated plan (Free / Pro / Enterprise); subject
to the caller's plan requests/day and requests/minute limits.
Args:
ticker: Optional symbol to show just one row, e.g. "SPY". Leave blank for all.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations (plan availability, rate limits, parameter behavior). Annotations already indicate safe read-only operation, so the bar is lower but description still adds value.
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 succinct (<5 sentences), front-loaded with purpose, and structured with a header, bullet metrics, and parameter Args section. No wasted words.
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 low complexity (1 optional param, no output schema), the description covers input and usage well. However, it does not describe the output format or how to interpret return values, which agents might need.
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?
With 0% schema coverage, the description meaningfully explains the ticker parameter: optional, leave blank for all, example 'SPY'. This adds clarity beyond the schema's default value.
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 performs backtesting of quant strategies across a watchlist, listing specific metrics (Sharpe ratio, max drawdown, win rate, total return). This distinguishes it from sibling tools like analyze_stock or get_monte_carlo.
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 mentions availability to all authenticated plans and rate limits, but does not explicitly advise when to use this tool versus alternatives like get_monte_carlo or get_ai_prediction. The context is clear but lacks explicit exclusions.
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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Each tool targets a distinct purpose: individual quant analyses (AI prediction, IV, Monte Carlo, option pressure, equity curve, risk scan), aggregation (analyze_stock), output generation (images, report), and account registration. No two tools are likely to be confused.
All tool names follow a uniform verb_noun snake_case pattern (e.g., get_ai_prediction, generate_stock_images, register_account). The convention is applied consistently across the entire set.
10 tools is well within the ideal 3-15 range and covers the full stock-analysis workflow: data gathering, analysis, aggregation, and report generation. Each tool contributes distinct functionality without bloat.
The tool set comprehensively covers the domain of quant stock analysis: predictive models, backtesting, options/IV analysis, risk scanning, aggregated analysis, and visual/report outputs. No obvious dead ends or missing core operations for the intended purpose.