Assess Study Validation Pair

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

The description goes far beyond the annotations, detailing side effects (appends an audit event), output specifics (returns blockers, evidence hash, includes diagnostics when available), and limitations (external calculations unverified, decision_grade_ready false). It also clarifies it never clears champion blocks. This is consistent with annotations (readOnly=false, so writes are allowed; destructive=false, so non-destructive) and adds substantial behavioral context that an agent would otherwise not know.

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 verbose and runs several paragraphs, packing many technical clauses without clear section breaks or bullet points. While it front-loads the main purpose, the density and jargon (e.g., 'retained_sampling limits', 'holdout_provenance report', 'fresh_validation') make it hard to scan. It would benefit from restructuring to highlight key behaviors and parameter roles.

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 complexity and the presence of an output schema, the description is quite comprehensive. It covers the checks performed, optional behaviors, return values, and limitations. It mentions what is not verified and that decision_grade_ready remains false. It does not elaborate on error conditions or when the tool might fail, but the output schema and annotations cover the expected response, and the description provides sufficient operational context for an agent to invoke 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?

Schema description coverage is 0%, so the description must compensate, but it does not clearly map each parameter to its meaning. It references 'full-model revision' and 'validation pair' which hint at full_run_id and validation_run_id, but it does not explicitly explain study_id, policy_id, or how the IDs relate to the checks described. The description is dense but lacks a clear parameter-to-purpose mapping, leaving agents to infer from context.

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 verb and resource ('Assess a validation pair and append a prediction-access audit event') and lists the checks performed. It also clarifies what it does NOT do ('does not fit, accept or promote') and mentions 'never clears champion blocks', distinguishing it from promotion/acceptance tools. However, it doesn't explicitly name sibling tools like evaluate_study_run or validate_study_recipe, so differentiation is implicit rather than explicit.

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 rich context about when checks apply (e.g., 'when evidence is available', 'Optional prelaunch retained_sampling limits require complete native records', 'Optional require_policy_review checks...') and exclusions ('does not fit, accept or promote', 'decision_grade_ready stays false'). It implies usage scenarios but does not explicitly say 'use this when you need X instead of Y'. The alternative tools are not named, so the guidance is contextual rather than comparative.

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