get_admissions

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

No annotations are provided, so the description carries the full burden. The verb 'Get' implies a read operation, but the description does not disclose any behavioral traits beyond that—no mention of pagination, return format, edge cases (e.g., empty results), or potential side effects. It is a simple read, but the disclosure is minimal and does not add value beyond the tool name.

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 a single, well-structured sentence. The core action and object ('Get every hospital admission') are front-loaded, followed by the scoping parameter. There is no fluff or redundant phrasing, making it highly concise and easy to parse.

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 simple one-parameter read tool with an available output schema, the description is reasonably complete: it states what the tool does and which parameter drives it. It does not cover usage nuances or alternatives, but the simplicity of the tool and the presence of an output schema mitigate the need for more elaborate context. A small gap remains in not guiding when to choose this over siblings.

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 0%, so the description must compensate. The description clarifies that subject_id is the patient identifier ('by subject_id'), which adds meaning beyond the bare integer type in the schema. However, it provides no additional constraints or format details (e.g., positive integer, required), so the compensation is partial. For a single-parameter tool, this is a baseline level of semantic clarification.

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?

Description states a specific verb ('Get'), a specific resource ('every hospital admission'), a clear scope ('for one patient'), and the exact parameter ('by subject_id'). It clearly distinguishes from sibling tools like find_patients (patient demographics) and get_labs (labs), leaving no ambiguity about what this tool returns.

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 implies usage: use this when you need admissions for a patient. However, it gives no explicit context about when to prefer this over siblings or any exclusions (e.g., 'for patient demographics use find_patients'). The guidance is minimal, relying on inference rather than direct instruction.

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