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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal the analytic nature of the tool (modeling, testing, inferring), but it does not state whether it is read-only, whether it mutates state, or how the different inputs trigger different behaviors.

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 a single dense sentence that front-loads the core capabilities. There is no filler; every phrase adds information about what the tool computes.

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?

The tool has 9 parameters, 0% schema coverage, and no annotations, so a one-sentence description is insufficient for correct invocation. It does not explain how parameters interact, what inputs are required beyond agent_id, or how the outputs should be interpreted.

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 connects 'BDI' to beliefs/desires/intentions and 'inverse planning' to action_trace and candidate_goals, but it leaves agent_id, ground_truth, and witness_event semantically unexplained.

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 uses specific verbs ('model', 'test', 'infer') and identifies a concrete resource: agents' BDI mental states. The Sally-Anne false-belief test and inverse-planning details clearly distinguish it from generic cognitive siblings like cognitive.infer or cognitive.resolve_intent.

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 intended use is implied: it is for theory-of-mind tasks involving BDI modeling, false-belief evaluation, and goal inference. However, it never explicitly states when to prefer this tool over related alternatives, and no exclusions or preconditions are mentioned.

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