athena_joint_factor_belief

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does state that the tool is read-only, which is a key safety trait, but it does not describe the return format, error behavior, or any side effects, leaving significant gaps for the agent.

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?

Two sentences with key information front-loaded. Every word earns its place; there is no filler or redundancy.

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 three parameters with nested objects and no output schema, yet the description does not explain the return value, provide examples, or detail the belief composition. For a complex tool, this is inadequate for an agent to invoke it correctly without further assumptions.

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 maps parameters to concepts: axes (with a 2..5 bound), compatibility multipliers, and optional state likelihoods, adding meaning beyond the bare schema names. However, it does not clarify the structure of nested objects or how to construct them, so the enrichment is partial.

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 identifies the tool's function: composing a bounded finite joint science-twin belief with specific inputs (factor axes, compatibility multipliers, optional state likelihoods). It also explicitly distinguishes it from a universal joint posterior, which helps differentiate it from related tools.

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 when constructing a bounded joint belief across 2..5 axes and notes it is not a universal joint posterior, but it does not explicitly state when to prefer this tool over alternatives or provide exclusions beyond that. No alternative tools are named.

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