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. While 'Get' implies a read-only operation, the description doesn't address critical behavioral aspects: whether this requires authentication, rate limits, data freshness, computational cost, error handling, or what format the projections return. For a complex tool with 10 parameters and no output schema, 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. Every word earns its place: 'Get' (action), 'climate change projections' (resource), 'from CMIP6 models' (source), 'for different warming scenarios' (context). No redundancy or fluff.

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 (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., time series data, statistical summaries), how projections are generated, or prerequisites like data access. For a climate projection tool with significant parameter requirements, more context is needed to guide effective use.

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 70%, providing a solid baseline. The description adds minimal parameter semantics beyond the schema—it mentions 'warming scenarios' which might relate to models or parameters, but doesn't clarify how parameters like 'daily' array choices or model selections affect the projections. The description doesn't compensate for the 30% coverage gap in undocumented parameters.

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 climate change projections from CMIP6 models for different warming scenarios.' It specifies the action ('Get'), resource ('climate change projections'), and source ('CMIP6 models'), which is more specific than just restating the name. However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'seasonal_forecast' that might also provide climate-related data.

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 sibling tools like 'weather_forecast', 'seasonal_forecast', and 'ensemble_forecast' that might overlap in functionality, there's no indication of this tool's specific niche (e.g., long-term climate projections vs. short-term weather forecasts). Usage context is implied but not stated.

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