attach_outcome_dataset

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

Beyond the annotations, the description adds meaningful behavioral context: it warns that outcome joins are association-only and explicitly flags that authorization is required. This is valuable for an agent deciding whether and how to invoke the tool. Annotations already cover idempotency and destructiveness, so the additional caveats push this above baseline.

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 with zero filler. It front-loads the primary purpose and then packs the most important constraints—association-not-causation and authorization—into a compact, readable warning. Every sentence earns its place.

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, full schema coverage, and the presence of an output schema, the description covers the key behavioral caveats an agent needs. It could theoretically mention the effect on downstream analysis or concrete when-not-to-use scenarios, but it is sufficiently complete for a tool that attaches a dataset.

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 100%, so the schema already documents both parameters, including examples. The description adds no parameter-level detail, which is acceptable because the schema carries the burden. Per the baseline for high schema coverage, a 3 is appropriate.

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 and resource: 'Attach external outcome dataset for join analysis.' It clearly conveys what the tool does and is distinguishable from the sibling tools, which are mostly read/get operations. It does not explicitly contrast itself with a specific sibling, but the verb+resource combination is unambiguous.

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 use when an external outcome dataset needs to be attached for join analysis, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. The causation warning and authorization requirement provide context, but no comparative guidance is given versus siblings like get_outcome_correlation or create_experiment.

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