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

Annotations already establish read-only, closed-world, non-destructive behavior, so the description adds real value by disclosing that rows are source-labeled, that methodologies differ across the three datasets, and that cross-source summation is invalid. It does not cover result size, default ordering, or pagination behavior, which is the main remaining behavioral gap.

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?

Three tightly packed sentences that front-load the resource and sources before the methodological caveat. Each sentence carries information, though the mid-sentence list of sectors and datasets is dense enough to scan slowly, and a short parenthetical on defaults or limits would have been more useful than further compression.

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 does describe the shape of returned rows (source-labeled, sectoral, per-person/intensity indicators) and units. However, for a five-parameter tool with zero schema documentation it should also explain the limit parameter, the meaning of 'WLD', and what an unfiltered call returns.

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 carry the load. It usefully explains what each source contains (Climate TRACE sectoral, Climate Watch historical, OWID per-person/intensity) and gives example sector values, but it never explains the 'limit' parameter, the 'WLD' enum value, or the expected sector string format, leaving meaningful gaps.

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 names a specific resource (national GHG emissions for the six GCC states by year) and enumerates the three underlying datasets with their units and basis (MtCO2e, 100-year). It also narrows scope with 'National aggregates only.' It does not explicitly differentiate itself from siblings such as gcc_series, keeping it at a 4.

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?

Usage is implied rather than stated: 'National aggregates only' and 'never add one source to another' tell the agent the intended scope and a key analytical constraint. There is no explicit when-to-use guidance, no mention of when another tool (e.g. gcc_series) would be preferred, and no note on what happens when filters are omitted.

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