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 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish that this is a read-only, idempotent, non-destructive, open-world operation. The description adds useful behavioral context beyond the annotations by stating plan availability and that requests are subject to per-day and per-minute rate limits.
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 front-loaded with the core purpose and returned metrics, then adds plan limits and argument guidance without excessive text. The Args block is slightly formal but still efficient for a one-parameter tool.
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 read-only tool with one optional parameter, rich annotations, and no output schema, the description supplies the needed context: what is computed, which metrics are returned, who can use it, rate-limit caveats, and how ticker affects the result. Nothing essential for correct invocation appears to be missing.
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%, so the description must carry parameter meaning for the single optional ticker argument. It does this well by explaining that ticker can isolate one symbol, giving an example like "SPY", and stating that leaving it blank returns all rows.
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 states a specific action and resource: backtesting quant strategy performance across a watchlist. It also names the returned metrics, making the tool's output purpose clear. It does not explicitly distinguish itself from sibling analysis tools such as get_monte_carlo or get_ai_prediction, so it falls short of a 5.
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?
Usage is implied by the backtesting purpose, and the ticker argument explanation clarifies how to get one row versus all rows. However, it gives no explicit guidance on when to choose this tool over related siblings like get_monte_carlo or analyze_stock.
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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