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qgis-dev-probe-mcp

qgis-dev-probe-mcp

by yo-1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_plugins

Retrieve installed QGIS plugins with versions and active states to verify which extensions are available and enabled before testing plugin behavior.

Instructions

Installed plugins with version and active state.

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_plugins"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

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?

No annotations are provided, so the description carries the full disclosure burden; it only implies read-only via the name and mentions two returned fields. It says nothing about scope (all installed plugins vs. those enabled for the current user/workspace), ordering, or whether the operation has any side effects.

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?

A single short sentence with no filler, and the key attribute (active state) is front-loaded. It is a sentence fragment rather than a complete statement, but nothing is wasted.

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?

For a zero-parameter list tool with no output schema, the description conveys the rough shape of the return value (versions and active flags). It still leaves scope and enumeration completeness unstated, which is the main thing an agent would want to know before relying on the result.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-level confusion is possible.

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

Purpose3/5

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

The fragment names the resource (plugins) and the returned attributes (version, active state), and the name supplies the "list" verb. It stops short of an explicit verb+object statement and does nothing to distinguish it from siblings like reload_plugin, which operate on the same resource.

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?

There is no indication of when to call this versus reload_plugin or any other plugin-related sibling, no prerequisites, and no exclusion conditions. The agent must infer usage entirely from the tool name.

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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