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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description does not need to re-state safety. It adds valuable behavioral context: 'weighted 24h avg CO2', data sources 'EIA + Electricity Maps', 'hourly' updates, and 'history since 1973'. It also explains the greenness_flag logic (renewables >50%). This goes beyond the structured annotations without contradicting them.

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 the returned data and key fields, then provides usage guidance and update frequency. It is somewhat dense but every clause contributes useful information. The length is justified given the number of output fields and use cases covered.

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 only one optional parameter and no output schema, the description covers the return fields, data sources, update cadence, and relevant use cases. It does not explain the exact units of avg_co2_intensity or the meaning of grid_load_status, but the description is sufficiently complete for an agent to select and invoke the tool 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 coverage is 100% for the single optional 'days' parameter, which has a detailed description in the schema. The tool description itself does not mention the parameter or provide additional meaning, so it does not add beyond the schema. Baseline 3 is appropriate when the schema fully documents the parameter.

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 'Returns' and the resource (a 0-100 renewable-share and grid carbon intensity summary), and lists specific output fields. It is distinctive enough to differentiate from likely sibling tools focused on other data domains.

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 states 'Call when the user asks about carbon intensity, grid emissions, renewable share, clean energy, or electricity greenness, or when timing carbon-aware scheduling of compute, ML training, or batch workloads for Scope 2 reporting.' This provides clear trigger contexts and a concrete use case, effectively guiding when to select this tool.

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