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 'long-range seasonal forecasts' but doesn't specify data sources, accuracy, update frequency, rate limits, authentication needs, or what the output looks like (since there's no output schema). For a complex forecasting tool with 12 parameters, this is a significant gap in behavioral context.

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 without unnecessary words. It directly states what the tool does ('Get long-range seasonal forecasts') and key constraints ('for temperature and precipitation up to 9 months ahead'), making it easy to parse quickly. Every word earns its place.

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 the complexity (12 parameters, no annotations, no output schema, and many sibling tools), the description is incomplete. It lacks crucial context: no output details, no behavioral traits (e.g., data latency, reliability), no differentiation from siblings, and minimal parameter guidance. For a forecasting tool with significant parameter interplay, this leaves the agent under-informed.

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 description coverage is 75%, which is relatively high, so the baseline is 3. The description adds minimal parameter semantics beyond the schema—it implies parameters for location and time range but doesn't explain relationships (e.g., how 'forecast_days' interacts with 'start_date'/'end_date') or provide usage examples. It doesn't compensate for the 25% coverage gap (e.g., units like 'temperature_unit' are only in 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 clearly states the tool's purpose: 'Get long-range seasonal forecasts for temperature and precipitation up to 9 months ahead.' It specifies the action ('Get'), resource ('seasonal forecasts'), and scope ('temperature and precipitation up to 9 months ahead'). However, it doesn't explicitly differentiate from siblings like 'climate_projection' or 'ensemble_forecast' that might also provide forecast data, which prevents a perfect score.

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. With many sibling tools (e.g., 'weather_forecast', 'ensemble_forecast', 'climate_projection'), there's no indication of what makes 'seasonal_forecast' unique or when it's preferred over others. This lack of context leaves the agent guessing about appropriate usage scenarios.

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