post_trade_review

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

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds value by noting it does not call an LLM (cost=$0) and that the agent writes the review based on structured evidence, providing transparency about cost and 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 concise, front-loaded with the main purpose, structured as a list of outputs, and ends with an important cost note. Every sentence is relevant and well-organized.

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?

The tool has no output schema, but the description thoroughly lists all output components (wins, losses, patterns, recommendations, open_positions_to_review). It is complete for its purpose, though more detail on the structure of each component could be beneficial.

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 coverage is 100% with clear descriptions. The description adds that max_trades defaults to 20 to avoid token explosion, which provides additional rationale beyond the schema. This improves parameter understanding slightly.

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 the tool's function as a past N days trade review that structures data from three other tools into a 'lessons learned' draft, with explicit output components (wins, losses, patterns, recommendations, open_positions_to_review). This distinguishes it from siblings like analyze_trade_performance or get_trade_journal.

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?

The description gives clear context: it's for reviewing trades over a period, combining multiple data sources. It implicitly distinguishes from siblings by naming them. However, it does not explicitly state when to avoid this tool or list alternatives.

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