Evolution Run Backtest

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

Annotations already declare the safety profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false), so the description adds 'vectorized' as a mild performance hint. However, it does not explain why a seemingly pure computation is flagged readOnlyHint=false (e.g., writes a log or caches results), leaving that inconsistency unaddressed.

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?

Two tight paragraphs, front-loaded with the core action, no filler. The trailing metrics list is partly redundant given an output schema exists, costing a small amount of 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?

With an output schema present, the return-value list is redundant, and full schema coverage handles parameters. The remaining gap is behavioral: no guidance on when to choose this over sibling analysis tools and no explanation of the non-read-only side effects implied by annotations.

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 100%, so all four parameters are already documented in the schema, including pattern_dict coming from discover_patterns. The description's 'pattern dict (from discover_patterns)' merely restates the schema, adding no new syntax or constraints.

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 (backtest) and resource (a candidate pattern against historical OHLCV data), and identifies the upstream producer of the input ('from discover_patterns'). An agent can immediately distinguish this from siblings like evolution_evolve_strategy or validate_strategy.

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 implies a workflow by naming discover_patterns as the source of pattern_dict, but never states when to backtest versus validate_strategy or evolve_strategy, nor any prerequisites or exclusions. Usage is implied rather than prescribed.

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