OpenWeatherMap 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 conversion action but lacks details on error handling, rate limits, authentication needs, or what happens with ambiguous queries. For a tool with no annotation coverage, this is a significant gap in transparency about its operational behavior.

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 extremely concise and front-loaded, consisting of a single, clear sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it efficient and easy to parse for an AI agent.

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 of geocoding (which can involve ambiguous inputs and varied outputs), the lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, error conditions, or how reverse geocoding works (coordinates to location name). For a tool with no structured support, more contextual detail is needed to be fully helpful.

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 both parameters ('query' and 'limit') with clear descriptions. The tool description adds no additional meaning beyond what the schema provides, such as examples or edge cases. With high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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: converting location names to coordinates or vice versa. It specifies the verb 'convert' and the resource 'location', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-location-info', which might provide similar or overlapping functionality, preventing 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. It doesn't mention any context, prerequisites, or exclusions, and with sibling tools like 'get-location-info' available, there's no indication of how this tool differs or when it should be preferred. This lack of comparative guidance limits its utility for an AI agent.

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