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AI Manga & Anime Generator - Mangii MCP

Get generation job

manga.get_job
Read-onlyIdempotent

Poll a generation job from manga.create_story, manga.continue_story, or manga.regenerate_panel when async=true. Repeat until status is succeeded or failed. Prefer async=false so you do not need this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesJob id from a previous generate result. Example: job_ab12cd34.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoJob id.
errorNoMachine error code such as invalid_request, insufficient_credits, or quality_forbidden.
resultNoFinished panel fields when status is succeeded.
statusNoJob status.
messageNoHuman-readable error. Never includes stack traces or internal paths.
next_stepNoWhat to call next.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context by disclosing the polling loop and terminal states (succeeded or failed), which is not visible from annotations alone. It does not mention timeout or rate-limit behavior, but those are less critical for a read-only poll.

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 pack the operation, trigger condition, polling loop, terminal statuses, and a recommendation to prefer async=false. Every phrase earns its place and is front-loaded with the core action.

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?

With a single well-documented parameter, a provided output schema, and safety annotations, the description fully covers what an agent needs to invoke it correctly. It explains the source of job_id, when to poll, how long to poll, and even the preferred alternative.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents job_id with an example. The description reinforces that job_id comes from a previous generate result but does not add new syntax or formatting details beyond the schema, so the baseline 3 applies.

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?

States a specific verb 'poll' and resource 'generation job', and identifies the exact producing tools (manga.create_story, manga.continue_story, manga.regenerate_panel) plus the async=true condition. This clearly distinguishes it from content-creation and billing siblings like manga.create_story or manga.buy_credits.

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

Usage Guidelines5/5

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

Explicitly says when to use it: when async=true, and instructs to repeat until status is succeeded or failed. It also gives an exclusion by recommending 'Prefer async=false so you do not need this tool.' This is clear usage guidance, though it does not name a sibling alternative.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: story creation, continuation, regeneration, panel listing, story listing, style listing, credit lookup, credit purchase, and job polling. Even the credit-related tools are clearly separated by purchase versus query. There is no meaningful overlap that would confuse an agent.

Naming Consistency5/5

All tools consistently use the manga. prefix with snake_case verb_noun names like create_story, list_panels, and regenerate_panel. The pattern is uniform across the entire set, making the API predictable and easy to navigate.

Tool Count5/5

Nine tools is a well-scoped size for a manga generation server: three creation-oriented tools, three listing tools, two credit tools, and one async job helper. Each tool serves a clear, non-redundant role in the workflow.

Completeness4/5

The core manga creation lifecycle is well covered: create, continue, regenerate, list stories, and list panels, plus style discovery and credit management. The only notable gaps are destructive operations like deleting stories or panels and perhaps a story detail endpoint, but these are not essential to the primary generation workflow.