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

Annotations already declare read-only, non-destructive, closed-world behavior, so the safety bar is covered. The description goes beyond them by disclosing the scorecard's replay windows (1, 5, 20 sessions over five years benchmarked against SMH), which meaningfully characterizes the output content.

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 sentences, front-loaded with the return content before the usage guidance. The first sentence is dense but every clause (replay windows, benchmark) carries information; nothing is redundant.

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 read-only, two-parameter, no-output-schema tool, the description sufficiently explains both what is returned (signals plus historical return scorecard) and when to call it. Nothing needed by an agent to invoke it correctly is missing.

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%, with limit (default 20, max 50) and symbol (omit for whole universe) fully documented in the schema. The description adds no parameter-level detail beyond that, so the 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 — returns rule-based technical signals logged on tracked companies — plus the scorecard of historical replay returns. This is clearly distinguishable from siblings like get_price_history or get_market, which cover price data rather than logged signals and their backtest performance.

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 when to use it: user asks about recent technical signals or how a signal type has performed historically. It gives clear context but names no alternatives or exclusions, so an agent must infer that other tools cover raw price/news data.

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