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SWMM-MCP

SWMM-MCP

by kLabUM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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change_storm

Modify stormwater models to apply a 24-hour SCS Type 2 design storm with specified rainfall depth for hydraulic analysis.

Instructions

Modify the model's storm event to an 24-hour SCS Type 2 design storm of a given depth. depth: Total rainfall depth in inches :return: Name of the new storm or 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>",
      "depth": "<depth>"
    },
    "name": "change_storm"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYes
depthYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It implies a mutation ('Modify') but doesn't specify permissions, reversibility, side effects, or rate limits. The mention of returning 'Name of the new storm or error message' adds some context, but overall, critical behavioral traits are missing for a tool that modifies model data.

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 brief and front-loaded with the core action, using two sentences efficiently. However, the second sentence 'depth: Total rainfall depth in inches' is somewhat redundant with the first, slightly reducing conciseness, but overall it's well-structured with minimal waste.

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

Completeness3/5

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

Given 2 parameters with 0% schema coverage and no annotations, the description partially compensates by explaining 'depth' and hinting at return values. The presence of an output schema means the description doesn't need to detail return values fully, but gaps in parameter and behavioral context keep it at an adequate but incomplete level.

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?

Schema description coverage is 0%, so the description must compensate. It explains 'depth' as 'Total rainfall depth in inches', which adds meaning beyond the schema's type 'number'. However, it doesn't address 'model_name' at all, leaving half the parameters undocumented. This partial coverage results in a low score.

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 action ('Modify the model's storm event') and specifies the resource ('to an 24-hour SCS Type 2 design storm of a given depth'), which distinguishes it from siblings like 'plot_rainfall' or 'run_model'. However, it doesn't explicitly differentiate from potential similar tools (e.g., 'add_storage' might also modify aspects), keeping it at 4 rather than 5.

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), exclusions, or compare to siblings like 'plot_rainfall' or 'upload_model', leaving the agent to infer usage from context 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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