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SWMM-MCP

SWMM-MCP

by kLabUM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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plot_rainfall

Visualize rainfall timeseries data from EPA SWMM stormwater models to analyze precipitation patterns and support hydraulic system understanding.

Instructions

Displays a timeseries plot of the model's rainfall to the user. Returns the name of the timeseries or an error message.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_name": "<model_name>"
    },
    "name": "plot_rainfall"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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 tool 'displays' a plot and returns a name or error, but lacks critical details: whether this is a read-only operation, if it requires specific permissions, what format the display uses (e.g., GUI, file), or any side effects. For a visualization tool with zero annotation coverage, this leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two clear sentences: one stating the action and one about the return. It's front-loaded with the core purpose. However, the second sentence about return values could be integrated more smoothly, and there's room to add brief usage context without losing efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (visualization tool with 1 parameter), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It omits parameter semantics, behavioral details (e.g., display mechanism, error conditions), and ties to sibling tools. The return value mention is helpful but insufficient for full contextual understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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, and the tool description provides no information about the 'model_name' parameter. It doesn't explain what a model name is, how to obtain valid names (e.g., from 'list_models'), or any constraints. The description fails to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Displays a timeseries plot of the model's rainfall to the user.' This specifies the action (displays), resource (rainfall timeseries plot), and target (user). However, it doesn't explicitly differentiate from sibling tools like 'plot_model_map' or 'plot_output_data' which suggests similar visualization functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 prerequisites (e.g., needing an existing model), contrast with other plotting tools like 'plot_model_map' or 'plot_output_data', or specify appropriate contexts. 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.

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