get_study

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

With no annotations, the description carries the full burden. It discloses the return structure (full study object with key fields) and contrasts with the summary tool, adding behavioral context. However, it doesn't explicitly state side effects (e.g., read-only nature) or error behavior, though the verb 'Fetch' implies safety. Minor gap, hence 4.

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 succinct and well-organized: purpose statement, usage context, return summary, and args. Every sentence adds value, with no redundancy. It is front-loaded with the core action and resource.

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?

Despite having an output schema, the description still lists key returned fields, giving the agent a quick mental model. It covers purpose, usage, return, and parameter semantics. No significant gaps are evident for a simple one-parameter fetch 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?

Schema description coverage is 0%, so the description fully compensates by providing an 'Args' section: 'study_id: The unique identifier of the study.' This unambiguously defines the only parameter, exceeding the schema's bare string type.

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 the full study configuration including sections, status, objectives, persona, product overview, and metadata. It also explicitly differentiates from the sibling get_study_summary by contrasting raw data vs. human-readable overview.

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?

Provides explicit when-to-use guidance: 'Use this when you need raw study data for programmatic processing or to check specific field values.' It also names the alternative: 'For a human-readable overview to present to the user, prefer get_study_summary.' This is clear and actionable.

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