get_equity_curve

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety bar is largely covered. The description adds one piece of genuine context not in the annotations — that the data is backtested/simulated rather than live — but nothing about data history depth, rate limits, or freshness. The lengthy Returns block restates the output schema rather than adding behavior.

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 when-to-use list is well front-loaded, but roughly half the text is a field-by-field Returns breakdown that duplicates the already-present output schema. That section is padding the agent does not need, diluting an otherwise tight definition.

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?

With a single required param, full schema coverage, an output schema, and rich annotations, the description covers the essential selection question: what the tool yields and why you'd call it. The only notable omission is any pointer to a contrasting sibling 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?

Only one parameter, and its schema description is fully covered (uppercase ticker, no company names, with examples). The description adds no format or normalization detail beyond that, so baseline 3 applies.

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?

States a specific verb and resource: retrieve backtested equity curves and performance metrics for rule-based strategies on a single stock. That scope is clearly distinct from prediction- or options-oriented siblings, though no sibling tool is named to sharpen the contrast.

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?

A dedicated 'Use this tool when:' block gives three concrete scenarios (strategy evaluation, risk-adjusted metrics comparison, historical context for a multi-leg options position). It gives strong positive guidance but never states when not to use it or points to an alternative like analyze_stock or get_monte_carlo.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.