MCP Weather 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 states the tool returns 'A formatted multi-day forecast summary,' which hints at output format but lacks details on error handling, rate limits, authentication needs, or data sources. For a tool with no annotations, this leaves significant behavioral gaps.

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 front-loaded with the core purpose in the first sentence, followed by a structured 'Args' and 'Returns' section. Every sentence adds value without redundancy, making it efficient and easy to parse. The formatting enhances clarity without unnecessary verbosity.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema exists, so the description needn't detail return values, but it lacks context on sibling tool differentiation and behavioral traits. It meets basic needs but has clear gaps in usage guidance and transparency.

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 adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'city' is the 'Name of the city' and 'days' is the 'Number of days to forecast (1-5, default 3),' including range and default value. This compensates well for the schema's lack of descriptions, though it doesn't cover edge cases like city name formatting.

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 a multi-day weather forecast for a city.' It specifies the verb ('Get'), resource ('weather forecast'), and scope ('multi-day' and 'for a city'). However, it doesn't explicitly differentiate from the sibling tool 'get_weather', which likely provides current weather rather than a forecast.

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 the sibling 'get_weather' or any alternatives. It mentions the basic functionality but offers no context about appropriate use cases, prerequisites, or exclusions. The agent must infer usage from the tool name and description alone.

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