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

The description adds context beyond annotations by specifying the data source (FRED INDPRO), the calculation methodology, and update frequency ('Updates: monthly'). This complements the readOnlyHint=true annotation without contradicting it.

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 three sentences, front-loaded with the core return value, followed by use cases and update frequency. No redundant information.

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 simple read-only tool with a single optional parameter and no output schema, the description adequately conveys the purpose, the score's range and fields, the data source, and update cadence. The optional 'days' parameter is fully described in the schema.

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?

The input schema has 100% description coverage for the single optional 'days' parameter, so the description need not repeat it. The description itself focuses on the tool's purpose and outputs, which is sufficient given the schema's thoroughness.

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 states 'Returns a 0-100 US industrial-production momentum score (FRED INDPRO, recent value vs trailing-mean % deviation, monthly since 1922) with momentum_score, recent_value, and deviation_pct.' This clearly identifies the tool's function and outputs, distinguishing it from generic data tools.

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 explicitly says 'Call when the user asks about US industrial output acceleration, manufacturing activity, factory production trends, or business-cycle strength, or when timing sector rotation into industrial and manufacturing equities or overweight/underweight shifts.' This provides clear when-to-use context, though it does not mention alternatives or exclusions.

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