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

With no annotations, the description carries the full behavioral burden and does so richly: it declares determinism ('no AI in the decision'), typical latency ('usually under a second'), the advisory nature of the verdict, how sanctions name matches are handled ('engage=false with manual review, never a finding'), billing ('counts as 1 check with a key; free tier without one'), and the full return shape including a receipt_id and user-facing 'read'. It also links to governing rules.

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 front-loaded with the core purpose and decision, then covers inputs, outputs, and operational rules. It is long but justified by the tool's complexity (16 optional parameters, no output schema). The single dense paragraph could be more scannable, but every sentence contributes actionable 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 the complexity (16 parameters, no output schema, no annotations), the description is nearly complete: it explains the verdict, return fields, determinism, edge-case handling, billing, and how to present results. It does not cover every parameter's mutual precedence or the role of less common fields (e.g., signal, ip_asn), but the schema handles those, leaving only minor gaps.

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 100%, so the schema already documents all 16 parameters thoroughly. The description restates the main input options (domain, url, email, phone, company_name, context, amount, policy) and notes the default policy, but adds little meaning beyond what the schema provides; baseline 3 applies when schema does the heavy lifting.

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 states a clear verb and resource: it gives a fast 'engage-or-not' answer for screening a lead, sender, vendor or website. It is specific about the decision it returns (engage true/false with risk, confidence, reasons, mitigations), but it does not explicitly distinguish itself from siblings like qualify_counterparty or verify_business.

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?

It clearly states the context of use ('before you reply to, onboard, pay or buy from someone') and enumerates supported contexts (lead, inbound_email, partnership, vendor, purchase, marketplace_seller, agent_action). It also tells the agent how to act on the result (act on engage=true; on engage=false, show reasons and mitigations). However, it does not name alternative tools or state when not to use this one.

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