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

Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world, and the description adds substantial behavioral context: open access without a key, ANP licensing, partial coverage from 2024 onward, `fonte`/`escopo` fields, `cobertura_manual`, and the 12-month variation restriction. These details materially help an agent interpret the results correctly.

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 front-loaded with purpose and units, and most sentences carry operational or caveat information rather than filler. It is dense and uses long parenthetical clauses, so structure could be cleaner, but the length is largely justified by the source-coverage and license caveats.

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 7-parameter tool with no output schema, the description explains both input modes and the shape of returned data: latest-month operator aggregates, national share, 12-month variation, field-level results, monthly series, and coverage fields. It also covers source, license, citation, and the non-investment-advice disclaimer, leaving little an agent needs before calling it.

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?

With 0% schema description coverage, the description carries the burden and explains most parameters: `periodo` and `de`/`ate` use AAAA-MM, `cnpj` is 14 digits, `operador` is the ANP operator name, `campo` selects a field, and `nivel=campo` changes granularity. Some details remain implicit, such as the full accepted values for `nivel` and exact matching rules, so it is strong but not complete.

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 resource and scope: oil and natural gas production by operator and field from ANP open data, including daily average units. It is clearly distinguishable from sibling tools such as tarifas_energia or financial-data tools because it names the exact domain, source, and measurements.

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?

It gives clear usage modes: no arguments returns latest-month operators, `nivel=campo` returns fields, `periodo` selects another month, and `cnpj`/`operador`/`campo` return monthly series clipped by `de`/`ate`. It does not state explicit exclusions or name an alternative tool, so it stops 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.