climate_projection

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

No annotations are provided, so the description must fully disclose behavior. It does not mention data latency, processing time, that projections are model-based and not real-time observations, or any limitations on scenario coverage.

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?

A single sentence is concise but lacks structure. It front-loads the core purpose but could benefit from breaking down into key aspects (e.g., supported models, scenario types, geographic scope) for easier parsing.

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?

Given 10 parameters (6 required) and no output schema, the description is incomplete. It does not explain coordinate limits, date range restrictions, model combinations, or how 'warming scenarios' translate to input parameters.

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?

The description adds no parameter-specific information beyond the input schema. With 70% schema coverage, the baseline is 3, but the description fails to enhance understanding of parameters like 'disable_bias_correction' or coordinate system details.

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 identifies the tool as retrieving climate change projections from CMIP6 models for different warming scenarios. It distinguishes from weather forecast siblings, although it could mention the specific focus on long-term projections versus short-term forecasts.

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?

No guidance on when to use this tool versus alternatives like seasonal_forecast or ensemble_forecast. Lacks information on prerequisites, such as valid warming scenario selection or model constraints.

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