maginary-mcp

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list_parameters

Retrieve Maginary prompt-DSL parameters, filtered by category or status as needed. Returns matching parameters along with category and status taxonomy maps for reference.

Instructions

List Maginary prompt-DSL parameters.

Args:
    category: Restrict to one category (e.g. ``composition``, ``video``,
        ``model``, ``outpaint``). Call with no filters once — the response's
        ``categories`` / ``statuses`` maps are the full taxonomy.
    status: Restrict to one status (``live``, ``mostly-dead``,
        ``unimplemented``).
    include_reserved: When False (default) drop ``unimplemented``
        (recognized-but-blocked) parameters from the result.

Returns:
    A dict with ``count``, ``source`` (``live`` vs. ``bundled-snapshot``),
    ``categories`` / ``statuses`` (the filter taxonomy), and ``parameters``
    (the array of matching entries).

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_parameters"
  }
}

Input Schema

Name	Required	Description	Default
`status`	No
`category`	No
`include_reserved`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Sep 5, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It covers filtering behavior, the effect of include_reserved, the response structure, and the important live vs. bundled-snapshot source distinction. It does not mention auth requirements or rate limits, but these are not implied by the tool's read-only listing nature.

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 well-structured with Args and Returns sections, front-loads the purpose, and avoids filler. It is slightly repetitive around the 'unimplemented' status concept across status and include_reserved, but remains efficient.

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

Completeness4/5

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

For a listing tool with no required parameters and an output schema available, the description covers the key call patterns, filter semantics, and return keys. It does not discuss pagination or entry-level details of the parameters array, but the output schema likely covers those, and the description provides enough to invoke the tool correctly.

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

Parameters5/5

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

Schema coverage is 0%, so the description must fully explain the parameters. It does: category gives concrete examples and points to the taxonomy response, status lists allowed values, and include_reserved clarifies the default behavior with 'unimplemented' parameters. All three parameters receive meaningful, usable semantics.

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 states a specific verb and resource: 'List Maginary prompt-DSL parameters.' It clearly conveys the tool's core function, though it does not explicitly differentiate itself from sibling tools like search_parameters or get_parameter.

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?

The description gives clear usage context for each filter and explicitly advises calling with no filters once to get the full taxonomy. It does not, however, state when to prefer this tool over alternatives such as search_parameters.

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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