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

With no annotations, the description carries the full behavioral burden. It discloses the upstream data source (Open-Meteo archive vs forecast) and licensing (CC BY 4.0), but it does not explain read-only behavior, rate limits, authentication needs, or what the score represents.

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 short, but it is a dense, jargon-heavy fragment lacking clear front-loaded structure. Terms like 'Kalshi weather fix' and 'ensemble mean anomaly' are not ordered in a way that helps an agent quickly parse the tool's purpose.

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?

There is no output schema, so the description should explain what the bias score returns and how to interpret it. Instead, it only lists ingredients and does not cover output format, score scale, or the relationship between ticker mapping and city inputs.

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 input schema has 100% description coverage for all three parameters, so the schema already documents city codes, model hints, and days_back. The description adds no parameter-level meaning beyond that baseline.

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 identifies a weather-bias calculation for Kalshi weather markets and lists data sources/components, but it does not state a clear verb or explain the actual operation. It also fails to distinguish this tool from the sibling `weather_bias` or `kalshi_markets` tools.

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?

There is no explicit guidance on when to use this tool, when not to use it, or which sibling tool is the alternative. The phrase 'Kalshi weather fix' implies a market-adjustment context but does not give the agent usable selection criteria.

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