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

Annotations already declare read-only, idempotent, non-destructive hints, and the description adds valuable behavioral details: the multi-stage validation process, the split return structure, and the expectation that most queries yield empty results. This goes well beyond the annotations and prepares the agent for common outcomes.

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 purpose, followed by the validation source, and then the return behavior and typical outcome. Every sentence contributes unique information without redundancy.

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?

With one parameter, good annotations, and no output schema, the description covers the essential context: what the tool does, how results are structured, and the common empty-list case. It provides sufficient information for an agent to invoke it correctly and interpret results.

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 schema provides 100% coverage of the single 'slug' parameter with a detailed description, including examples and expected return format. The tool description reinforces this but adds no new information beyond what the schema already states. Baseline 3 is appropriate.

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 fetches 'statistically-validated leading/lagging relationships for an indicator,' which is a specific verb+resource. It further distinguishes from siblings by naming the five-filter scanner pipeline and explicitly returning 'as_leader' and 'as_follower' lists. This makes the tool's purpose unmistakable.

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 on when to use the tool: to obtain validated leading/lagging relationships for a specific indicator. It also implies when not to worry about empty results ('Most ... return empty lists'). However, it doesn't explicitly name alternatives or state when to prefer sibling tools, so it falls just short of a 5.

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