atlas_topics

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

With no annotations, the description carries full burden. It explains the tool is based on BERTopic clustering and returns topics, terms, and counts. However, it does not disclose potential staleness, computation time, or any side effects. The description is truthful but could provide more operational context.

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 two focused sentences. The first explains the tool's mechanism and output, the second gives usage context with examples. No redundant or unnecessary information.

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?

Given no parameters and no output schema, the description adequately covers what the tool does and its output (topics, terms, counts). It could specify the number of topics or update frequency, but is largely complete for the tool's simplicity.

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 tool has zero parameters, so the baseline is 4. The description does not need to elaborate on parameters, and it correctly avoids misleading information.

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 performs BERTopic clustering on incident text corpus to discover topics with representative terms and incident counts. It also provides concrete examples of use (narrative mapping for election shutdowns, etc.), differentiating it from siblings like atlas_search or atlas_timeline.

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 for narrative mapping and gives examples, but does not explicitly state when not to use or compare to alternatives. The context is clear enough for an AI agent to understand the tool's purpose, but lacks explicit exclusions.

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