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maginary-mcp

by io.github.maginaryai
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List parameters

list_parameters
Read-onlyIdempotent

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNoFilter by status: live, mostly-dead, or unimplemented.
categoryNoFilter by category, e.g. composition, video, model, outpaint.
include_reservedNoInclude unimplemented (blocked) parameters.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context: it explains the meaning of include_reserved (dropping unimplemented parameters) and describes the response keys (count, source, categories/statuses, parameters). This goes beyond annotations and helps the agent understand the tool's output and filter semantics.

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?

The description is well-structured with Args and Returns sections, front-loading the core purpose in the first sentence. It is concise yet comprehensive, with no redundant information. Each sentence adds value, making it easy for an agent to parse quickly.

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?

The tool has an output schema, and the description explicitly outlines the return dict structure, covering count, source, categories/statuses, and parameters. All three parameters are optional and documented, and the description provides usage guidance. An agent has everything needed to invoke the tool correctly without missing critical details.

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 100%, so the baseline is 3. The description enriches parameter meaning by providing concrete examples for category (e.g., composition, video) and explaining that include_reserved defaults to False and drops unimplemented parameters. It also suggests using no filters to get the full taxonomy, which adds usage context beyond the schema's basic descriptions.

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 verb 'List' and the resource 'Maginary prompt-DSL parameters', making the tool's purpose explicit. It also details the filtering options and return structure, distinguishing it from siblings like search_parameters and get_parameter without ambiguity.

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 provides clear usage guidance, notably 'Call with no filters once — the response's categories / statuses maps are the full taxonomy.' This tells the agent when to use the tool and what to expect. However, it does not explicitly contrast with sibling tools like search_parameters, so it lacks explicit exclusions or alternative selection criteria.

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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