SWMM-MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns contents (implying a read operation) and includes a constraint about bracket usage, adding some behavioral context. However, it lacks details on permissions, error handling, rate limits, or output format, which are important for a tool with parameters and an output schema. The description does not contradict any annotations, as none exist.

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 adds critical usage notes. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 has 2 parameters, 0% schema coverage, and an output schema exists, the description is reasonably complete. It covers purpose, parameter semantics, and usage guidelines, and the output schema likely handles return values. However, as a read operation with no annotations, it could benefit from more behavioral details like error cases or data format, slightly reducing completeness.

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 0%, so the description must compensate. It explains that 'model_name' refers to a given model and 'section' is a section of the input file, with additional guidance to avoid brackets and use 'get_input_sections' for valid sections. This adds meaningful context beyond the bare schema, though it could specify format examples or constraints more explicitly. With 0% coverage, this is above the baseline of 3.

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 the contents of a section of the input file for a given model.' It specifies the verb ('Returns'), resource ('contents of a section'), and scope ('input file for a given model'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'get_input_sections' or 'get_model_info', though it references 'get_input_sections' for context.

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 clear usage context by stating 'Do NOT include brackets in the section name' and 'Refer to the tool get_input_sections for a list of sections.' This gives explicit guidance on parameter formatting and references an alternative tool for obtaining valid section names. However, it does not specify when to use this tool versus other siblings like 'get_model_info' or 'get_output_variables', limiting full differentiation.

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