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 only states the core operations (logical deduction and Bayesian inference) but says nothing about return formats, side effects, prerequisites, failure modes, or whether it is deterministic. For a tool with seven optional parameters and no safety hints, this is a significant gap.

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 with no wasted words, but it is drastically under-specified. While it is front-loaded with the purpose, it omits critical information about parameters and behavior, making it more of an under-specification than a concise, effective description.

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 seven optional parameters and an output schema, yet the description provides almost no context. It does not explain what inputs are required for each mode, how evidence is used, what the query and query_var parameters do, or what the tool returns. The description is far too sparse for a tool of this complexity, especially with no annotations to fill the 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 0%, and the description does not mention any of the seven parameters (facts, nodes, query, rules, evidence, query_var, probabilistic). It fails to explain the role of each parameter or how they relate to the two inference modes, leaving the agent to rely entirely on the raw schema with no semantic help.

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 specific action (perform) and two distinct inference types: logical deduction with Horn clauses and exact probabilistic Bayesian network inference. This clearly conveys the tool's purpose, though it does not differentiate it from the sibling tool cognitive.infer, which likely serves a similar role.

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 provided on when to use this tool versus alternatives. It does not mention any conditions that would favor logical over probabilistic inference, nor does it reference sibling tools like cognitive.infer for comparison. Agents are left to infer appropriate usage from the schema alone.

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