Open-Meteo MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data source ('Canadian weather service GEM model') and geographic coverage ('Canada and North America'), but doesn't address critical behavioral aspects: whether this is a read-only operation, rate limits, authentication requirements, data freshness, or what the response format looks like. For a weather API tool with 11 parameters, this leaves significant gaps in understanding how the tool behaves.

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 communicates the core purpose. It's appropriately sized for a weather forecast tool, though it could potentially be more front-loaded by mentioning the geographic scope earlier. There's no wasted language, and every word contributes to understanding what the tool does.

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 (11 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain what the tool returns, how forecasts are structured, time ranges covered, or data resolution. For a weather forecasting tool competing with 15 siblings, users need more context about output format, data quality, and use cases to select this tool appropriately.

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 provides no parameter-specific information beyond what's already in the schema. However, with 100% schema description coverage and detailed parameter documentation (including enums, defaults, and constraints), the schema does the heavy lifting. The baseline score of 3 is appropriate since the schema fully documents all 11 parameters, though the description adds no additional parameter context.

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 weather forecast from Canadian weather service GEM model with high-resolution data for Canada and North America.' It specifies the verb ('Get'), resource ('weather forecast'), and geographic scope ('Canada and North America'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'gfs_forecast', which likely provide similar weather data from different sources.

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 15 sibling tools including 'weather_forecast', 'gfs_forecast', and 'ecmwf_forecast', there's no indication of when GEM model forecasts are preferable (e.g., for Canadian regions, high-resolution needs, or specific weather variables). The description mentions 'high-resolution data for Canada and North America' but doesn't make this a clear usage recommendation.

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