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?
Annotations already declare readOnly, safe, idempotent. Description adds context: plan limits, request limits, behavior of leaving ticker blank vs providing one. No contradictions.
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?
Five lines, front-loaded purpose, clear Args section. Every sentence adds value.
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?
Simple tool with one optional param and no output schema; description covers all needed: purpose, metrics, plan info, parameter usage.
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?
Description fully explains the only parameter (ticker): optional, example 'SPY', default behavior (show all symbols). Compensates for zero schema description coverage.
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?
Clear verb and resource: 'Backtest performance of the quant strategy across your watchlist' with specific metrics listed (Sharpe ratio, max drawdown, etc.). Distinguishes from siblings like 'analyze_stock' or 'get_mc' by focusing on overall backtest performance.
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?
States availability to authenticated plans and subject to rate limits. Implicitly when to use (for backtest stats) but does not explicitly contrast with alternatives. Still clear enough for selection.
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 has a clearly distinct purpose, from individual analytics like AI prediction and IV radar to aggregation and report generation. No overlapping functionality.
All tool names follow a consistent verb_noun pattern using snake_case, with verbs like get_, generate_, and analyze_. No mixing of conventions.
9 tools is well-scoped for a quant finance server, covering diverse analytics and outputs without being excessive or sparse.
Covers major quant finance analyses and reporting. Minor gap: no raw data access tools, but the surface is comprehensive for the intended use case.