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

Annotations already cover read-only, idempotent, and non-destructive hints, so the bar is lower. The description adds non-obvious behavioral context: the required prerequisite calls in strict order and the directive to call before writing SQL. This goes beyond annotations and meaningfully informs the agent.

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 concise and well-structured: it opens with the core purpose, then covers prerequisites, usage triggers, and composition with other tools. Every sentence adds value, with no filler or 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?

Given the zero parameters, the presence of an output schema, and strong annotations, the description covers all essential operational details: prerequisites, ordering, when to invoke, and combinability. Nothing critical is missing for an agent to correctly use this 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 input schema is empty with zero parameters, so the baseline is 4. There are no parameters to document, and the description appropriately avoids adding irrelevant placeholder information. The schema coverage is trivially complete.

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 loads an earnings workflow for specific topics (EPS surprises, beat/miss, estimates, revenue). The verb 'load' and resource are explicit, and the domain-focused topic list distinguishes it from sibling workflow tools. The enumeration of user intents further clarifies 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 provides strong when-to-use guidance with an explicit list of user queries (e.g., 'did X beat estimates', quarterly earnings) and states a prerequisite call sequence (REQUIRES get_database_schema then get_query_patterns first, in that order). However, it does not explicitly state when not to use or name alternative workflow tools as replacements, so it falls just short of the top tier.

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