research

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the internal process (probing distinctive words, merging hits) and the exact output shape (best threads in full, Blind's AI summary, comments that address the question). This goes well beyond a generic 'research' label, though it does not mention error conditions or rate limits.

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 efficient and well-structured: a one-sentence summary, a two-sentence mechanism/outcome explanation, and example queries. Every sentence earns its place and there is no redundant fluff. The key purpose is front-loaded.

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 the tool has an output schema and three simple parameters, the description covers the core purpose, mechanism, and return contents clearly. It is slightly incomplete for an agent because it does not explain the meaning of max_posts or explicitly differentiate from sibling tools, but it is still usable as-is.

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 must compensate. It effectively explains 'company' and 'question' through the purpose sentence and the example prompts, but 'max_posts' is never mentioned or contextualized. The compensation is partial: the two main parameters are clear, but the third is not addressed.

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 opens with a specific verb-resource pairing: "Answer a question about a company by pulling the most relevant threads." It clearly differentiates itself from the sibling tools (company_topics, company_posts, read_post, find) by describing a cross-thread research capability that merges hits and returns a curated answer with AI summary and relevant comments. The natural-language examples reinforce what the tool does.

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 strongly implies when to use this tool: whenever you have a natural-language question about a company, with examples showing the expected phrasing ("Ask naturally: ..."). However, it does not explicitly name alternatives or state when NOT to use this tool in favor of a sibling, so it stops short of full routing guidance.

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