Open-Meteo MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'forecast uncertainty' but doesn't explain how this is represented (e.g., probability distributions, confidence intervals). It also omits critical details like rate limits, authentication requirements, data freshness, or response format. For a complex forecasting tool with 10 parameters, this is insufficient.

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, efficient sentence that front-loads the core purpose. It wastes no words and directly communicates the tool's function. Every part of the sentence ('Get ensemble forecasts', 'showing forecast uncertainty', 'with multiple model runs') contributes essential information.

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?

For a complex forecasting tool with 10 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., uncertainty metrics, time series), how ensemble results are aggregated, or any behavioral constraints. The agent lacks sufficient context to understand what the tool actually returns or its operational characteristics.

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 doesn't add any parameter-specific information beyond what's in the schema. With 70% schema description coverage, the schema already documents most parameters well (e.g., latitude/longitude descriptions, enum lists for models/hourly/daily). The description's mention of 'multiple model runs' aligns with the 'models' parameter but doesn't provide additional context. Baseline 3 is appropriate given the schema does most of the work.

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 tool's purpose: 'Get ensemble forecasts showing forecast uncertainty with multiple model runs.' It specifies the verb ('Get'), resource ('ensemble forecasts'), and key feature ('showing forecast uncertainty with multiple model runs'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'ecmwf_forecast', which likely provide single-model 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or specify scenarios where ensemble forecasts are preferable (e.g., for uncertainty quantification vs. deterministic forecasts). Without such context, the agent must infer usage from the tool name and description alone.

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