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

Annotations already establish this is a safe read (readOnlyHint=true, destructiveHint=false), but the description adds substantial depth beyond them: which checks run (structure, declared constraints, enum membership, calculations), which do not (undeclared ranges, unit coherence, referential integrity, factor provenance), and a hard reporting rule gated on sector_policy appearing in `checked`.

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?

Front-loaded with the use case, then behavior, then caveats — a sensible order with no filler sentences. It is dense and packs several distinct obligations into one paragraph with backticked field names, which is efficient but slightly harder to scan than a structured list.

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?

Complexity is high (5 params, nested free-form payload, no output schema), and the description compensates well by naming the return fields an agent must read (`checked`, `checks_not_run`, `schema.coverage.constraintsDeclared`) and flagging `valid` as deprecated in favor of `schemaValid`. Only the `entity`/`domain` semantics remain unaddressed.

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 only 20%, so the description must carry the load. It clarifies `payload` and `sector` (guardrails) meaningfully, but `entity` and `domain` are never explained despite being required or present, leaving two of five parameters semantically undocumented anywhere.

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?

States a specific verb and resource: check a JSON emissions/footprint record against the Open Footprint model or a sector's guardrails. An agent immediately knows what it does. It does not, however, distinguish itself from the similarly named sibling `validate_data`, which is the main ambiguity risk.

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?

Explicitly opens with a when-to-use condition tied to a concrete artifact (a JSON payload about emissions/footprint) and adds a when-not clause ('Do not use for general data-quality questions without a payload'). It also scopes sector behavior ('with a sector, its guardrails'), giving the agent enough to choose correctly.

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