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

With no annotations provided, the description carries the full burden of behavioral disclosure. It covers generation timing (3-5 minutes), the human-in-the-loop activation, the free-preview vs. full-contract distinction, the legal disclaimer, and the mandatory accept_terms flag. This is exemplary for a zero-annotation 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 long but information-dense. It fronts the purpose and then logically orders the return value, timing, human-activation, preview/unlock, prerequisites, and the legal condition. Every clause serves a purpose, though it could be tightened slightly without losing value.

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 complex initiating tool with async behavior, prerequisites, and human involvement, this description is complete. It specifies return values (preview_id, URL), the follow-up polling action, the necessity of intake data, the mandatory terms acceptance, and the legal caveat. No critical information is missing for correct invocation.

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%, so the baseline is 3. The description adds meaningful context beyond the schema by linking deal_summary to get_intake_questions, specifying minimum required fields (parties), and emphasizing that accept_terms must be true after confirming with the user. It also hints at the contract_type source via list_contract_types in the schema, which is reinforced.

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 begins with a clear verb-resource pair ('Prepare a contract draft') and distinguishes it from siblings by describing the multi-agent AI engine, the preview mechanism, and the URL flow. It also references get_draft_status and get_intake_questions, making its role in the workflow explicit.

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 provides a clear usage sequence: collect deal_summary via get_intake_questions, require accept_terms:true, share the URL for human opening, then poll get_draft_status. It doesn't explicitly name alternatives like analyze_contract or get_checkout_link, but the workflow implications are unambiguous.

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