Create strategy (AI build)

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

With no annotations, the description carries full burden and does so thoroughly: explains 300s wait/timeout behavior, return fields, error handling with jobId, and constraints on indicator thresholds. It also advises to run backtest before optimization, disclosing the expected workflow.

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 dense but every sentence adds value: purpose, usage, parameters, prerequisites, return value, next steps, and timeout handling are all covered without redundancy. Front-loaded with the primary action and structured logically.

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 moderately complex tool with async behavior and dependencies, the description covers all essential aspects: what it does, when to use, prerequisites, parameters, return value, timeout handling, and follow-up actions. No significant gaps remain.

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?

Even though schema coverage is 100%, the description adds crucial semantic value: it explains what to include in the description parameter (instrument, entry/exit rules, position size), provides an example, and clarifies analysisId source. This goes beyond the schema's basic definitions.

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 it builds a brand new trading strategy from natural language, using a specific verb (build), resource (trading strategy), and method (AI builder). This distinguishes it from siblings like list_strategies or edit_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?

Explicitly says 'Use this when the user wants a new strategy' and provides concrete prerequisites (call get_capabilities first) and post-actions (evaluate with run_backtest). It also mentions optional grounding via analysisId, giving clear context for when to use this versus alternatives.

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