Weather bias vs recent mean — uses Open-Meteo archive+forecast and ticker mapping for Kalshi HIGH* fade edge.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | NYC|CHI|MIA|LAX or lat,lon = NYC default | |
| model | No | Model name for provenance | |
| days_back | No | Days back for anomaly, default 7 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description must fully disclose behavior. It mentions data sources but fails to explain whether the tool is read-only, requires authentication, or what side effects exist. The output format is not described.
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 a single sentence, making it concise, but it is not well-structured. It begins with the concept 'Weather bias vs recent mean' rather than a clear action statement, and the jargon reduces clarity.
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 three parameters, no output schema, and empty annotations, the description is insufficient. It does not specify what the tool returns, how the bias is computed, or any prerequisites, leaving an agent guessing about the tool's full behavior.
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% with adequate descriptions for all three parameters. The description adds context about the data sources and purpose, but does not significantly enhance the parameter explanations beyond the schema.
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 hints at computing weather bias vs recent mean using Open-Meteo data and a ticker mapping for Kalshi, but it lacks a clear action verb and relies on domain jargon ('HIGH* fade edge'). It is vague and does not clearly differentiate from sibling weather 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?
No guidance is provided on when to use this tool versus alternatives like weather_current_global or weather_forecast_grid. The description does not mention any specific use cases or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.