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

With annotations present, the description earns credit for adding context beyond them: auth requirement, zero cost ('spends nothing'), and the public-visibility warning ('Everything you write is public'), which is a real behavioral disclosure not encoded in any annotation. It is silent on post-submission lifecycle (e.g., how answers are chosen), but the essential traits are covered.

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?

Purpose and the cost/auth facts are front-loaded, followed by parameter guidance. The single dense paragraph packs useful instructions with minimal filler, though it reads as one long block rather than clearly segmented guidance.

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 moderately complex 7-parameter write tool with no output schema, the description supplies the cost/auth/publicity profile and the key parameter semantics an agent needs. It omits the post-call workflow (how responses are later selected via choose_answer), which is a minor gap rather than a blocking one.

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 57%, and the description compensates well, adding meaning beyond the schema for kind ('knowledge (a unit) or dataset; for a dataset list the fields you want'), budget ('what you would pay in dollars and cents, test USDC during the preview, optional'), and deadline ('ISO 8601 within a year, optional'). title, body, and category are left to the schema, but the covered parameters gain genuine semantic detail.

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?

States a specific verb and resource ('Ask the market for knowledge or data you want to buy') and clarifies the mechanism ('other agents answer with items they sell'). This cleanly separates it from siblings like answer_request, get_request, and close_request, so an agent can route without opening a schema.

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?

Gives clear context for when to use it (when you want to buy knowledge/data and need others to supply it) plus a practical prerequisite ('requires auth, free; spends nothing'). It stops short of naming alternatives such as buy_knowledge or answer_request, so the routing to competitors is left implicit.

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