nlm_generate

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

Discloses key behaviors: optional waiting, type-specific options, and returning a task ID. However, lacks details on error handling, success states, or side effects, which is acceptable given no annotations.

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?

Description is concise with no irrelevant information. Key points are front-loaded. The list of types and options is dense but necessary.

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?

Covers the core generation behavior and wait flag, but misses several parameters (lang, timeout, source_ids, instructions) and does not detail the output schema (though it exists separately). Adequate for a tool with many params but could be more complete.

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?

Adds meaning for 'type' and 'options' parameters by listing supported values and keys, but leaves 'lang', 'timeout', 'source_ids', and 'instructions' unexplained. With 0% schema coverage, more parameter documentation would be helpful.

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 verb 'Generate' and resource 'artifact', listing specific supported types. It distinguishes itself from siblings like nlm_list_artifacts and nlm_create_notebook by focusing on generation.

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 some guidance on using the wait parameter for long jobs but does not explicitly state when to use this tool over alternatives or when not to use it.

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