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QGIS MCP

QGIS MCP

by lefcgis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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List Processing Models

list_processing_models
Read-only

Retrieve a list of all registered QGIS Processing models, including their id, name, and group. Use this to identify which model to execute with run_model.

Instructions

List registered Processing models (the 'model' provider). Returns id, name, group for each. Use run_model to execute one.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_processing_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true, indicating no destructive side effects. Description adds that it returns id, name, group, which is useful but not essential beyond the output schema. No contradictions or additional behavioral traits are disclosed.

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, efficient and front-loaded. Every word adds value: identifies resource, specifies return fields, and links to sibling tool. No fluff or redundancy.

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

Completeness5/5

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

Given the tool has zero parameters and an output schema exists (documenting return structure), the description is fully complete. It explains the resource type (model provider) and purpose, which is sufficient for agent invocation.

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?

No parameters exist; schema coverage is 100% (empty). Description does not need to explain parameters. Baseline for zero params is 4, and description appropriately avoids adding irrelevant param info.

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?

Description clearly states it lists registered Processing models and specifies returned fields (id, name, group). It distinguishes from sibling run_model by advising to use that for execution. The verb "list" combined with resource "registered Processing models" is specific and unambiguous.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly mentions using run_model to execute a model, providing clear guidance on when to use this listing tool vs. the execution tool. However, it does not contrast with other listing tools like list_processing_algorithms or get_processing_providers, which could cause confusion. Still, the primary use case is well-stated.

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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