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

With no annotations, the description correctly identifies the tool as a search/read operation and mentions output fields (claim/cost volume, specialty/state/city) and data source (public CMS). However, it lacks details on pagination, error handling, or what happens when no results are found.

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 four sentences, starting with the core purpose and efficiently listing filters and output characteristics. Every sentence adds value without redundancy, though the price note could be seen as minor.

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 tool with 11 parameters and no output schema, the description covers the essential context: what it finds, how to filter, data source, and cost. It does not detail return structure beyond a brief mention, but schema descriptions handle most parameter details.

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?

Schema coverage is 82%, so parameters are mostly documented already. The description adds thematic context (e.g., 'brands resolve to the canonical generic') and overall purpose, but does not significantly expand on individual parameter meanings beyond the schema.

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 finds Medicare Part D prescribers by drug or therapeutic class at the NPI level, with the phrase 'core pharma targeting query' distinguishing it from sibling tools that focus on brand share shifts or class year-over-year changes.

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 lists filterable criteria (drug_class, drug, specialty, state, etc.) and provides examples like 'Ozempic' and 'GLP-1', giving agents clear context for when to use the tool. However, it does not explicitly state when to avoid this tool or list alternative tools for different scenarios.

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