get_document_topics

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

Description mentions it returns probabilities and top words, adding behavioral context beyond the schema. However, with no annotations, more details (e.g., that it is read-only) would improve transparency.

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 efficient sentences, front-loaded with key information, no unnecessary words or repetition.

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?

Adequately explains what is returned, but lacks mention of dependencies (e.g., requirement for a trained topic model). For a tool with no output schema, more detail on the response structure would help.

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 100% and parameter descriptions are clear. The description adds context about what the tool returns but does not add meaning beyond the schema for the parameters.

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?

Clearly states the verb 'Get', resource 'topics for a specific document', and what is returned (probabilities and top words). Distinguishes from siblings like list_docs or search_docs.

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?

No guidance on when to use this tool vs alternatives like cluster_documents_* or generate_topic_heatmap. Does not mention prerequisites such as needing a trained topic model.

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