analyze_metric

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

In the absence of annotations, the description discloses key behavior: it performs a z-score backtest with forward returns, implying a read-only computation. However, it does not specify whether it is read-only or mention any side effects.

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 two sentences, front-loads the core action, and avoids unnecessary words. Every part contributes to understanding the tool's purpose.

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 explains input and purpose well but is silent on output format or return values. Given no output schema, the agent lacks information on what the tool produces.

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?

With 100% schema coverage, the schema already documents parameters. The description adds value by explicitly mentioning 'z-score' and 'threshold', clarifying that threshold is a z-score threshold and connecting parameters to the tool's function.

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 it performs a single-metric z-score backtest with forward returns across 8 horizons, positioning it as the core factor discovery tool. This distinguishes it from sibling tools like analyze_metrics_composite and backtest_signal.

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 implies it is the go-to tool for testing a single metric's predictive power, but does not explicitly state when not to use it or mention alternative tools for multi-metric or signal-based analysis.

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