optimal_match

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

Beyond the readOnlyHint=true annotation, the description discloses substantial behavioral traits: it materializes a dense n_treated x n_control distance matrix, uses an O(n^3) Hungarian solver, and warns that memory and time degrade sharply. It also lists assumptions, pre-conditions, and failure modes. This goes well beyond what annotations or the schema convey.

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 long but well-structured with labeled sections: validation, scale warning, cost, assumptions, pre-conditions, and failure modes. It is front-loaded with the core purpose. The 'Validation: certified parity evidence' line is cryptic, and the failure-modes section appears truncated, which prevents a perfect score.

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?

For a computationally heavy matching tool, the description covers the essentials: scale limits, complexity, assumptions, pre-conditions, and failure-mode remedies. An output schema exists, so return-value documentation is not required here. However, the meaning of 'certified parity evidence' is unexplained, and the failure-modes list is cut off, leaving the guidance slightly incomplete.

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 baseline is 3. The description adds meaning by specifying that covariates should be pre-treatment, that the treatment should be binary or low-cardinality, and that covariate overlap is required. This contextualizes the covariates and treatment parameters beyond the schema's terse descriptions, though it does not address every parameter in detail.

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 opens with a precise statement: 'Optimal 1:1 matching via the Hungarian algorithm.' This names the operation, the resource/units being matched, and the algorithm. It also distinguishes itself from sibling matching tools by explicitly naming sp.psm and sp.match as alternatives for larger samples, so an agent can tell it apart without opening the schema.

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 is explicit about when NOT to use it ('Do NOT use when: either arm has more than ~10,000 units'), names the alternative tools to use at that scale, and provides assumptions and pre-conditions that define appropriate use. This is strong, actionable guidance rather than vague context.

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