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SWMM-MCP

SWMM-MCP

by kLabUM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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change_conduit

Modify circular conduit diameter in EPA SWMM stormwater models to adjust hydraulic capacity and system performance.

Instructions

Changes the diameter of a circular conduit in the model.

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>",
      "conduit_name": "<conduit_name>",
      "new_diameter": "<new_diameter>"
    },
    "name": "change_conduit"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYes
conduit_nameYes
new_diameterYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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 performs a change (mutation), but doesn't specify if it's destructive, requires specific permissions, has side effects, or details the response format. This is inadequate for a mutation tool with zero annotation coverage.

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

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded and appropriately sized for the task, earning a high score for conciseness.

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 the tool has an output schema (which handles return values) and moderate complexity (3 required parameters), the description is minimally complete but lacks depth. It covers the basic action but misses behavioral context and parameter details, making it adequate but with clear gaps for a mutation tool.

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 schema description coverage is 0%, meaning none of the 3 parameters are documented in the schema. The description only implies 'new_diameter' is a parameter without explaining what 'model_name' or 'conduit_name' refer to, their formats, or constraints. It adds minimal value beyond the schema's structure.

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 ('Changes') and the resource ('diameter of a circular conduit in the model'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'change_storm' or 'duplicate_model', which might also modify model properties, so it falls short of 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.

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 or conduit), exclusions, or related tools like 'change_storm' for context, leaving the agent to infer usage from the name 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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