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

With no annotations, the description carries full responsibility for behavioral disclosure. It mentions exactness for Bayesian inference but omits crucial details such as return format, side effects, prerequisites, or limitations. The description is too sparse to inform an agent about operational behavior.

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 sentence and is concise, but it lacks structure or front-loading of key details. It is under-specified rather than efficiently condensed, providing minimal 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?

Given 7 parameters, no schema coverage, and no annotations, the description is far from complete. It fails to explain how the two inference modes differ, which parameters are required for each, or what the output looks like. An agent cannot confidently invoke this tool correctly.

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 by explaining parameters. It does not mention any of the 7 parameters (facts, nodes, query, rules, evidence, query_var, probabilistic) at all, leaving the agent to rely solely on parameter names.

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 the tool performs logical deduction (Horn clauses) or exact probabilistic Bayesian network inference, with specific verb and resource. It distinguishes itself from sibling tools focused on strategy, planning, and other cognitive operations.

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?

No guidance is given on when to use this tool versus alternatives, nor when to choose logical deduction over probabilistic inference. The description simply states what it does without addressing mode selection or exclusions.

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