athena_causal_structure_bootstrap

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 does mention that the output is 'stable' and warns against interpreting stability as causal probability, but it does not address side effects, computation cost, or data format expectations. This is minimal disclosure.

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: the first states the core action and output, the second adds an important interpretive caveat. There is no wasted verbiage, and the structure is appropriately front-loaded.

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 the tool's moderate complexity (6 parameters, no annotations, no output schema), the description is too sparse. It omits parameter semantics, expected input format, and detailed behavioral outcomes, leaving significant gaps for an agent attempting to use the 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%, and the tool description does not explain any of the 6 parameters (e.g., samples, resamples, thresholds). The agent receives only type/range constraints with no semantic meaning, making it impossible to set appropriate values without external knowledge.

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 action ('Bootstrap') and the specific resource ('V7 heuristic association skeleton'), and specifies the output ('stable undirected/v-structure candidates'). This distinguishes it from sibling causal tools that may return different structures, and the caveat 'Stability is not causal probability' further clarifies its scope.

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 provides no guidance on when to use this tool versus alternatives like athena_structural_bootstrap_ensemble or athena_pc_stable_discover. It does not state prerequisites, exclusions, or typical use cases, leaving the agent to infer from the name only.

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