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. While it mentions the data source and coverage, it doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, accuracy characteristics, or what format the forecast data returns. For a complex 11-parameter tool with no annotations, this is a significant gap.

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 packs substantial information: action, source, data quality, and coverage. Every word earns its place with zero wasted verbiage. It's appropriately sized and front-loaded with the core functionality.

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 weather forecasting tool with 11 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how the forecast data is structured, what time ranges are available, or any limitations of the DWD ICON model. The agent would struggle to understand how to effectively use this tool beyond the basic parameter documentation.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'high resolution data' which relates to the forecast quality but doesn't provide additional context about parameter usage, relationships between parameters, or best practices for selecting hourly/daily variables.

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 action ('Get weather forecast') and specifies the source ('German DWD ICON model'), scope ('Europe and global coverage'), and data quality ('high resolution data'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'ecmwf_forecast' that might 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 multiple weather-related sibling tools (ecmwf_forecast, gfs_forecast, weather_forecast, etc.), there's no indication of what makes the DWD ICON model preferable or when it should be chosen over other forecast models.

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