Skip to main content
Glama
Sign In
SWMM-MCP

SWMM-MCP

by kLabUM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

plot_output_data

Visualize SWMM model output data by generating time series plots for specific objects and variables to analyze stormwater system performance.

Instructions

Displays a full timeseries plot to the user. Returns a summary of the data. model_names: List of model names to plot. object_type: the type of object to return. E.g. "node", "link", "subcatchment". Refer to the tool "get_output_variables" for a list of types. object_label: the label of the object in the output file. E.g. "J1", "S1" variable: the variable to return. E.g. "flow". Refer to the tool "get_output_variables" for a list of variables.

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_names": "<model_names>",
      "object_type": "<object_type>",
      "object_label": "<object_label>",
      "variable": "<variable>"
    },
    "name": "plot_output_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_namesYes
object_typeYes
object_labelYes
variableYes

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 full burden for behavioral disclosure. It mentions the tool displays a plot and returns a data summary, but doesn't specify format, interactivity, or storage implications. Critical behavioral traits like whether this is a read-only operation, if it modifies data, or has performance considerations are omitted. The description adds minimal context beyond the basic action.

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 efficiently structured with the core purpose first, followed by parameter explanations. Each sentence adds value without redundancy. However, the parameter explanations could be more integrated with the purpose statement rather than appearing as a separate list.

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?

For a 4-parameter plotting tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'full timeseries plot' means visually, what format the 'summary' returns, or how the plot relates to the data summary. Given the complexity of visualizing timeseries data across multiple models, more context about output behavior is needed.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate for all 4 parameters. It provides examples for each parameter (e.g., 'node', 'J1', 'flow') and references another tool for valid values, adding meaningful context beyond the bare schema. However, it doesn't explain relationships between parameters or constraints, leaving gaps in understanding how they work together.

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 full timeseries plot to the user. Returns a summary of the data.' This specifies both the visual output (plot) and data return (summary), distinguishing it from siblings like plot_model_map or plot_rainfall. However, it doesn't explicitly differentiate from other plotting tools beyond the timeseries focus.

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. While it references 'get_output_variables' for parameter values, it doesn't explain when plot_output_data is appropriate compared to other plotting tools (plot_model_map, plot_rainfall) or data retrieval tools (get_output_objects, get_output_variables). No exclusions or prerequisites are mentioned.

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

Deploy Server

Other Tools