company_topics

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

No annotations are provided, so the description carries the burden. It discloses that this is a read-only listing operation and explains the selection behavior: topics are 'Blind itself suggests' and are 'highest-signal entry points.' This adds meaningful behavioral context, although it does not mention potential edge cases such as companies with no suggested topics.

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 compact and front-loaded: the core statement appears in the first sentence, followed by a helpful purpose rationale and concrete examples. Every sentence earns its place, and the reference to company_posts adds actionable guidance without bloat.

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 one-parameter tool with an output schema, the description covers the main usage flow: retrieve suggested topics, then pass one to company_posts. The main gap is the missing company identifier format, but the overall tool behavior and integration with the sibling are sufficiently explained.

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 description coverage is 0%, so the description needed to compensate by explaining the `company` parameter, but it only says 'for a company.' It does not clarify the expected format (e.g., display name vs. slug), case sensitivity, or whether the company must match a known entity. The Roku example is about topic values, not the parameter format.

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 uses a specific verb ('List') with a clear resource ('discussion topics Blind itself suggests for a company'). It includes concrete examples (Roku's topics: india, wlb, culture, layoffs) and explicitly connects to the sibling company_posts by explaining that one of these topics should be used as its `topic`, making the tool's purpose unmistakable.

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 clearly tells the agent when to use this tool: to get the highest-signal entry points before calling company_posts. It explicitly names the relevant sibling and how the result should be consumed. It does not enumerate exclusions or alternatives like read_post/find/research, so it stops just short of full explicit when-not guidance.

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