train_nmf_topics

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

No annotations provided. Description states it 'Trains' but does not disclose side effects (e.g., model saving), prerequisites (documents must exist), or performance implications, leaving significant gaps for a mutating operation.

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?

Two concise sentences: first states the action and algorithm, second adds comparative value and output. No unnecessary words.

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 tool with 3 optional parameters, the description explains algorithm and output. Missing prerequisites and side effects, but gaps are minor given low complexity.

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?

All parameters are documented in the input schema with descriptions. The description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

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 trains an NMF topic model on documents, specifies the algorithm (NMF), and mentions it often produces more coherent topics than LDA, distinguishing it from sibling tools like train_lda_topics.

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?

Describes when to use (for coherence over LDA) but does not explicitly mention alternatives or when not to use, nor does it cover BERTopic or other models.

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