get_outcome_correlation

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

The description goes well beyond the readOnlyHint annotation by disclosing the statistical method (Pearson r), the data sources, and the critical interpretive boundary that results are ASSOCIATION/OBSERVATIONAL and never CAUSATION. This prevents misuse of the output and shows genuine behavioral transparency.

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?

Three concise sentences with no filler: the action, the computation method, and the interpretive caveat are all front-loaded and clearly organized. 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 has no parameters, has an output schema, and carries a readOnly annotation, the description covers the essential behavioral and methodological context. It explains what is computed, from what data, and how results should be interpreted, leaving no critical gaps for an agent to call it 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?

The input schema has zero parameters, so there are no parameter semantics for the description to clarify. The baseline for zero-parameter tools is 4, and the description adds context about what data is used without needing to document arguments.

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 uses a specific verb ('Correlate') and identifies the exact resources involved: micro-eval metrics, outcome quality scores, and cycle times via lineage. This clearly distinguishes it from sibling tools like get_lineage_summary or get_composite_score by stating the analytical method and result framing.

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 clearly implies when to use this tool: when the agent needs a Pearson correlation between micro-eval metrics and outcome/cycle-time data through lineage. It does not explicitly name alternatives or exclusions, but the context is strong enough that an agent would not confuse it with a general lineage or score retrieval tool.

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