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QGIS MCP

QGIS MCP

by lefcgis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Run Model

run_model

Run a QGIS Processing model using its registered ID or .model3 file path, with custom input parameters mapped by name.

Instructions

Run a Processing model by registered id (e.g. 'model:myflow') or by a .model3 file path. 'parameters' maps the model's input names to values (layer ids/paths, numbers, etc.).

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": "<model>"
    },
    "name": "run_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYes
parametersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only explains parameter mapping, but does not mention side effects, return values, error handling, or whether the model may alter project state.

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?

Two sentences efficiently convey purpose and parameter usage. No redundant information; every word adds value.

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?

Covers core usage but lacks details on return values, synchronous execution, prerequisites, or what happens if the model is invalid. For a 2-parameter tool without output schema, some additional context would be helpful.

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

Parameters4/5

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

Schema coverage is 0%, but the description adds meaning for both parameters: 'model' can be an id or file path, 'parameters' maps input names to values (e.g., layer ids/paths). This clarifies usage beyond the bare schema.

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

Purpose5/5

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

The description clearly states the tool runs a Processing model, specifying two distinct methods: by registered id or by a .model3 file path. This provides a specific verb and resource, distinguishing it from siblings like execute_processing.

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?

No guidance on when to use this tool versus alternatives such as execute_processing. It does not mention context, prerequisites, or when not to use it.

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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