SWMM-MCP

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. It states the tool returns information (implying a read-only operation) and mentions the need for exact model name input, but lacks details on permissions, error handling, rate limits, or response format. For a tool with zero annotation coverage, 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 highly concise and front-loaded: the first sentence states the core purpose, and the second provides essential usage guidance. Both sentences earn their place by adding value, with no redundant or verbose language. It efficiently communicates key information in minimal space.

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 low complexity (1 parameter, no annotations, but has an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for description details there. However, for a tool with zero annotation coverage and sibling tools that might overlap (e.g., 'list_models'), the description could better address context and differentiation to be more complete.

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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying that 'model_name' must be entered 'exactly as it appears in the list', which clarifies input requirements beyond the schema's type definition. However, it doesn't explain what constitutes a valid model name or reference the 'list_models' tool for context, leaving room for improvement.

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: 'Returns general information about a model.' It specifies the verb ('Returns') and resource ('general information about a model'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'list_models' or 'get_report_info', which prevents 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 minimal guidance: 'Be sure to enter the model name exactly as it appears in the list.' This hints at a prerequisite (knowing the exact model name) but offers no explicit advice on when to use this tool versus alternatives like 'list_models' or 'get_report_info'. There's no mention of use cases, exclusions, or comparisons to sibling tools.

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