query_threat_model

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

The description reveals AI-powered answering and the scope of topics (assets, attackers, etc.), adding value beyond the missing annotations. However, it lacks details on response format, rate limits, or potential costs, so the burden is partially met.

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 two concise sentences that front-load the purpose and scope. No wasted words; every sentence contributes essential 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 presence of an output schema and three required parameters, the description is nearly complete. It covers the tool's purpose and the kinds of questions supported. Minor gaps in parameter description and usage guidance are the only shortcomings.

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 67% (model_id and question described, server_version missing). The description does not mention any parameters, so it adds no additional meaning beyond the schema. Baseline 3 is appropriate.

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 clearly states it answers questions about threat models, specifying the resource ('existing threat model') and verb ('ask a question'). It distinguishes from sibling tools like 'get_threat_model' (retrieval) and 'assess_model' (assessment) by focusing on AI-powered Q&A.

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 implies usage for ad-hoc queries but does not explicitly state when to use it versus alternatives like 'get_asset' or 'list_findings'. No when-not or exclusion criteria are provided, leaving the agent to infer context.

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