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

Beyond the readOnly=true annotation, it adds key behavioral caveats: 'paper-trading data only (not real money)' and 'limit capped at 1000'. This gives important context about data scope and constraints without contradicting annotations.

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 well-organized with clear sections (Purpose, Triggers, When to call, Args, Caveats) and is front-loaded with the core purpose. Slightly verbose due to the extensive list of example triggers, but every section 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?

Given the 7-parameter schema, presence of an output schema, and many sibling tools, this description covers purpose, usage triggers, prerequisites, next steps, parameter semantics, and caveats. It is sufficiently complete without needing to restate output structure since an output schema exists.

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 0%, but the description compensates fully by explaining every parameter in plain language. It adds meaning for market_id (aliases), min_pnl/max_pnl (percentage), symbol (case-insensitive), and action_filter (allowed values), going well beyond the raw schema.

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 'Query paper-trading history with dynamic filters (action / P&L / time / symbol)', using a specific verb and resource. It distinguishes from siblings like get_winning_trades and get_losing_trades by framing this as the general history query with filters.

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?

Provides explicit 'When to call' scenarios (past trade review, single-symbol post-mortem, win-rate audits) and example user triggers. Does not explicitly mention when not to use or contrast with specific alternatives, which prevents a 5.

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