ofp_validate

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

Annotations only indicate a read-only, non-destructive operation; the description adds substantial behavior: API credit costs, four mutually exclusive outcomes, the not_applicable and unevaluable cases, schema.coverage meaning, and valid being null when evaluation didn't occur. This is far beyond what the annotations convey and directly shapes how an agent should interpret results.

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 dense but every sentence carries essential information, starting with the main purpose and then the outcome taxonomy and compliance caveat. It avoids repetition of schema fields and does not pad the text.

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?

With no output schema, the description carries the burden of explaining return behavior, and it does so thoroughly: the outcome enum, policy.missingInputs, schema.coverage, policy.ran, and valid semantics. For a five-parameter tool with nested payloads and no declared output schema, this is a complete and safely actionable description.

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 coverage is 100%, so the baseline is 3, but the description adds important behavioral semantics beyond field names: entityType defaults to the entity name and typically yields not_applicable, and canonical entities lack the e-ledger fields the guardrail rules test. This materially helps the agent provide a useful payload and interpret results.

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 ('Check a data payload against the canonical Open Footprint model') and clarifies scope by adding sector guardrails when a sector is named. It clearly distinguishes the tool from generic validation siblings like validate_data by anchoring it to the canonical Open Footprint model and policy outcomes.

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 gives clear context for when the tool applies (canonical Open Footprint validation, optional sector guardrails) and includes a hard usage rule: do not report compliance if policy.ran is false or outcome is unevaluable. It does not explicitly name alternatives or state when-not-to-use it in favor of a sibling, so it falls short of a 5.

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