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 the data source (NOAA GFS model) and coverage but does not disclose critical traits such as rate limits, authentication needs, data freshness, error handling, or whether it's a read-only operation. For a tool with 11 parameters and no annotations, this is a significant gap in transparency.

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 and adds relevant context (model and coverage), making it highly concise and well-structured for quick understanding.

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 tool's complexity with 11 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, usage guidelines, and output format, which are crucial for an AI agent to invoke the tool correctly. The high parameter count and absence of structured support make the description insufficient for full contextual understanding.

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%, meaning all parameters are documented in the input schema. The description adds no specific parameter details beyond implying forecast retrieval, so it does not compensate for any gaps. However, since the schema is comprehensive, the baseline score of 3 is appropriate as the description does not detract but adds minimal value.

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 US NOAA GFS model' specifies the verb ('Get') and resource ('weather forecast'), and it adds context about global coverage and high-resolution data for North America. However, it does not explicitly differentiate from sibling tools like 'weather_forecast' or 'dwd_icon_forecast', which might offer similar services, so it lacks sibling distinction for a top 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. It mentions the model (GFS) and coverage details but does not specify scenarios, prerequisites, or exclusions compared to siblings like 'ecmwf_forecast' or 'ensemble_forecast'. This absence of usage context leaves the agent without clear direction for tool selection.

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