nlm_ask

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

No annotations are provided, so the description carries full burden. It states the tool returns answer citations but does not disclose whether it is read-only, what happens if the notebook is missing, or any side effects. The behavioral transparency is minimal.

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, front-loaded sentence that efficiently conveys the core purpose. While it could add more detail without sacrificing conciseness, it avoids excessive verbosity.

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 annotations, no parameter details, and only a high-level outcome, the description is insufficient for complex usage. Although an output schema exists (not shown), the lack of behavioral context and parameter guidance leaves the agent underinformed.

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 schema has 2 parameters (query, notebook) with 0% description coverage. The description adds no meaning beyond the parameter names—e.g., it doesn't explain what a notebook is or how to format the query. This leaves the agent guessing about input requirements.

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 action ('Ask'), the resource ('source-grounded question'), and the outcome ('return answer citations'). This distinguishes it from sibling tools like nlm_summarize (which summarizes) or nlm_generate (which generates text).

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 no guidance on when to use this tool versus alternatives (e.g., nlm_summarize, nlm_research). It fails to specify prerequisites or context, leaving the agent to infer usage without differentiation.

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