factor_analysis

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

Annotations already convey readOnlyHint and non-destructive nature. Description adds value by detailing the statistical interpretation (alpha with t-stat significance threshold, R-squared, residual volatility) and listing the four factors. No contradictions.

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?

Description is concise yet thorough, using bullet points for factors and clear paragraphs. Front-loaded with main purpose, then details, then usage guidance. Every sentence 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 output schema exists (not shown but known from context), description doesn't need to itemize return values. It covers prerequisites, interpretation guidance (t-stat cutoff), and outputs conceptually, making it complete for the tool's 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 100% with a detailed description for the single parameter. The description provides additional context by noting the parameter typically comes from backtest_strategy output and including an example. Baseline 3 is appropriate as schema already does heavy lifting.

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 begins with a specific verb ('Decompose') and resource ('strategy or portfolio returns into Fama-French factors'), clearly stating what the tool does. It distinguishes from siblings by explicitly recommending usage 'after backtest_strategy to understand WHERE your returns come from — is it genuine alpha or just factor exposure?'

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 states when to use ('after backtest_strategy'), and provides conditions ('Needs at least 30 data points'). No explicit exclusions for alternatives, but context clarifies its purpose relative to sibling tools.

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