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

With no annotations, the description carries the full burden. It discloses that some methods fetch daily history, that failed tickers are dropped with a warning, and describes the output envelope. This is thorough and goes beyond a simple purpose statement.

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 compact and front-loaded: it opens with the core purpose, then lists methods and behaviors, and ends with output and pricing. Every sentence contributes value, and the structure is logical.

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 description covers input, methods, data fetching, failure handling, and output. It does not explain optional parameter defaults or which methods require top_n or range, leaving minor gaps for a tool of this complexity.

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%, so the description must explain parameters. It explains scores, method (with each option), and range (listing valid values). However, top_n is only mentioned as an output field, not explicitly defined as an input controlling selection, leaving a partial gap.

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 purpose: converting a ticker-score mapping into long-only portfolio weights with non-negative weights summing to 1.0. It lists specific methods, distinguishing it from sibling tools like compute_portfolio_stats or backtest.

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 provides clear context: it is for when you have a score mapping and want portfolio weights. It does not explicitly exclude alternatives or say when not to use it, but the input/output specification makes the use case evident.

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