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deepanime-mcp

by rocnubie
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Deep Anime AI MCP Server

Deep Anime: Character Art, Portraits, and Scene Design

MCP Badge Node License: MIT MCP Zero Config

A Model Context Protocol server that exposes the canonical Deep Anime AI knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://deepanime.org

🎨 About Deep Anime AI

Deep Anime (deepanime.org) is an AI image generation platform built specifically for anime-style artwork. It lets users create polished character art, portraits, and scene compositions by typing a text prompt, converting a photograph, or remixing an existing design. Rather than offering a generic image generator with an anime filter bolted on, the site provides a focused suite of tools designed around the anime aesthetic — covering everything from initial concept to final touch-ups within a single integrated workspace. Both casual creators experimenting with character ideas and more serious illustrators looking for fast iteration cycles can access the platform through tiered subscription plans.

Related MCP server: Muse AI Image MCP Server

Key Features

  • Text-to-Image (Prompt to Character Art): Describe a scene or character in plain text and the platform renders it as anime-style art, with pre-built prompt templates available in a gallery for inspiration.

  • Photo to Anime: Upload a photograph and convert it into stylized anime artwork, with attention to preserving facial details and likeness during the transformation.

  • Character Art Generator: Build original character designs from scratch, controlling style, pose, and visual identity without needing source photography.

  • Anime Image Editor: Adjust composition, color grading, and fine details on generated images directly in the platform, removing the need to export to a separate editor for basic refinements.

  • Character Face Swap: Swap or transplant character faces across different images while maintaining consistent visual style.

  • Character Consistency Across Angles: Remix workflows allow users to iterate on the same character — testing different outfits, poses, or angles — while preserving the core character identity across variations.

Use Cases

  • Portrait creation and stylization: Turning personal photos or reference images into anime-style profile pictures or character portraits.

  • Character design and development: Building out original characters for manga, webcomics, visual novels, or game concepts through iterative generation.

  • Scene and concept art: Composing multi-element scenes with specific backgrounds, lighting moods, and character placements for storytelling or world-building projects.

  • Outfit and pose iteration: Testing visual variations on an established character design — changing costumes, expressions, or viewpoints — without losing character consistency.

  • Content creation for social media: Producing anime-style illustrated content for profile images, fan art, or creative posts at a faster pace than hand-drawing.

Who Is It For

Deep Anime is aimed at two overlapping groups. The first is casual creators — fans, hobbyists, and social media users — who want to generate anime-style images of themselves, original characters, or fictional scenes without prior illustration skills or software knowledge. The second group is more production-focused: indie game developers, comic creators, visual novel writers, and digital illustrators who need a quick and consistent way to prototype character designs or generate reference art. The platform's pricing (with a Lite tier and a Pro tier) reflects this range, making entry-level access affordable while offering higher generation volumes and additional features to users with more demanding workflows.

Tools

list_styles

Return the canonical list of image-generation styles or presets the site exposes. (Deep Anime AI)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for Deep Anime AI.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Deep Anime AI (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://deepanime/styles — Supported image-generation styles and presets.

  • site://deepanime/pricing — Canonical pricing entry point.

  • site://deepanime/faq — Short FAQ generated from public site metadata.

  • site://deepanime/links — Canonical URLs to share with users.

Prompts

tell_me_about_deepanime

Summarize what the site is, who it's for, and how it works. — Deep Anime AI

try_image_style_deepanime

Recommend a starting image-generation style for a stated goal. — Deep Anime AI

Installation

Install via Smithery

npx -y @smithery/cli install deepanime-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/deepanime-mcp.git
cd deepanime-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "deepanime-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/deepanime-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

3 tools
get_pricingB

Return the canonical pricing entry point for Deep Anime AI.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

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

There are no annotations provided, so the description must disclose behavioral traits. It only states that the tool returns a pricing entry point, with no mention of whether it is read-only, requires authentication, or what the return format looks like. This lack of behavior disclosure is a significant gap.

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 a single sentence with no wasted words. It front-loads the action ('Return') and the resource, making it easy to scan. It is appropriately sized for a tool with no parameters and no complex behavior to describe.

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

Completeness2/5

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

With no output schema and no annotations, the description must fully explain what the tool returns. 'Canonical pricing entry point' is ambiguous—it does not clarify whether it is a URL, an object, or a string. For such a simple tool, this lack of detail makes the description incomplete for an agent to use confidently.

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 has zero parameters, so the input schema provides complete coverage. According to the rubric, a 0-parameter case receives a baseline score of 4 since there are no parameter semantics for the description to elaborate. The description appropriately does not attempt to add parameter information.

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 uses a specific verb ('Return') and identifies a clear resource ('canonical pricing entry point'), which distinguishes it from sibling tools like list_styles and get_official_links. However, 'pricing entry point' is slightly vague, as it could refer to a URL, an object, or a method, preventing a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives, and no exclusions or prerequisites are mentioned. The absence of any contextual hints leaves the agent to infer usage solely from the tool name and resource reference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_stylesA

Return the canonical list of image-generation styles or presets the site exposes. (Deep Anime AI)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It states what the tool returns, which is appropriate for a read-only list operation. However, it does not mention any additional behaviors such as whether the list is cached, sorted, or subject to change, leaving some ambiguity about the 'canonical' nature.

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 a single, concise sentence that conveys the essential information without any superfluous detail. It is well-structured and front-loaded with the action and resource, making it immediately understandable.

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's simplicity (0 parameters, no output schema), the description provides sufficient information to understand its purpose and expected return value. The sibling tool names further contextualize it as part of a collection of reference-related tools, making it complete in this context.

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 has zero parameters, so the description does not need to clarify parameter meanings. The baseline for 0-param tools is 4, and the description adds value by specifying what the returned list contains (styles/presets), aligning with the schema's empty properties.

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 tool's function: to return the canonical list of image-generation styles or presets. The verb 'Return' and resource 'canonical list' are specific, and the parenthetical '(Deep Anime AI)' adds context. This clearly distinguishes it from sibling tools like get_pricing and get_official_links.

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 does not explicitly mention when to use this tool versus alternatives, but the 'canonical' phrasing implies this is the authoritative source for styles, which provides clear context. Since the tool has no parameters and is a simple list retrieval, the intended use case is self-evident, though explicit exclusions are missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_styles
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TDQS

A3.8/5.0
Disambiguation5/5

Each tool targets a distinct piece of information: styles, pricing, and official links. There is no overlap or ambiguity in their purposes.

Naming Consistency5/5

All tool names follow a clean verb_noun pattern (list_styles, get_pricing, get_official_links), making them predictable and readable.

Tool Count4/5

With only 3 tools, the set is compact but well-scoped for an informational server. It fits the lower end of the ideal range, but each tool serves a distinct purpose and nothing feels redundant or excessive.

Completeness4/5

The domain appears to be 'canonical info about Deep Anime AI.' The toolset covers styles, pricing, and official links, which are the core needs. Minor gaps such as detailed style info or FAQ are possible, but the surface is reasonable for its purpose.

Resources

Unclaimed servers have limited discoverability.

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