athena_structure_partial

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions 'uncertainty-preserving' as a property but does not disclose side effects, output format, reversibility, or any requirements. For a transform-like operation, this is insufficient.

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 sentences long, front-loads the core function, and adds a useful caveat. Every word earns its place, with no filler or repetition.

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?

Despite having 6 parameters, no output schema, and no annotations, the description provides only a terse one-line conversion statement. It does not explain the graph representation, parameter relationships, or expected results, making it inadequate for correct invocation in most contexts.

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 parameters are entirely undocumented in the schema. The description does not mention any parameter names, formats, or meanings, leaving the agent to infer from names like 'samples' and 'support_threshold' without any elaboration. This is a critical gap.

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: 'Convert V8 bootstrap association stability into an uncertainty-preserving o-o partial graph.' This clearly identifies the tool's function and resource. The negative clause 'Not FCI/PAG/CPDAG theorem' helps distinguish it from sibling causal discovery tools, though the jargon may be opaque to general agents.

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 context by specifying the input ('V8 bootstrap association stability') and what it is not ('Not FCI/PAG/CPDAG theorem'), but it does not explicitly state when to prefer this tool over alternatives or provide exclusions. The negative distinction gives a hint but no direct guidance on when to use or avoid.

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