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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context: daily rebuilds, the nature of the data (factual, no recommendations), conditional personal stacks when an account is linked, and the return fields. This goes well beyond annotations.

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, front-loaded with the core definition, and includes the use case and a factual-data disclaimer. Every sentence earns its place; no filler. It is appropriately sized for a zero-parameter tool.

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 no parameters, no output schema, and annotations covering safety, the description is complete for an agent to call it correctly. It explains what is returned, the conditional account behavior, and the intended use. Nothing critical is missing for a list tool.

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 tool has zero parameters, so the schema is empty and coverage is 100% by definition. Per the rubric, a 0-parameter tool gets a baseline of 4. The description adds no parameter semantics, but none are needed.

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 lists curated stacks of Indian companies, rebuilt daily, and specifies what is returned (slug, rule, member count, changes). It distinguishes itself from siblings like run_stack and stack_changes by focusing on listing rather than executing or showing changes. The 'Use for' phrase reinforces its purpose.

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 gives a concrete use case: 'Use for which companies clear several quality screens right now.' It does not explicitly mention alternatives or when not to use it, but the context is clear enough for an agent to decide. A small gap: no explicit exclusions or comparison to run_stack, but the use case is directive.

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