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AI Swap Face MCP Server

by rocnubie
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AI Swap Face MCP Server

AI Swap Face - Online AI Face Swap Tool

MCP Badge License: MIT Stdio Transport Read Only MCP Node

A Model Context Protocol server that exposes the canonical AI Swap Face 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://aiswapface.online

🎨 About AI Swap Face

AI Swap Face is a browser-based face-swapping tool that lets users replace faces in photos and videos using neural network processing. No software installation is required — users upload an image or video clip, select a source face, and the service produces a composited result with automatic color matching and blend controls. The platform supports single-face and multi-face detection, meaning it can identify and swap multiple faces within a single group photo in one pass. Processed files are automatically deleted after 24 hours, and results are delivered without watermarks on supported tiers. A REST API with webhook support is also available for developers who want to embed the capability into their own applications.

Related MCP server: Nano Banana 2 MCP Server

Key Features

  • Photo and video face swapping — supports still images (JPG, PNG, WebP, HEIC) and video clips up to 10–60 seconds depending on account tier, with output from 720p up to 4K resolution.

  • Multi-face detection — automatically locates every face in a group photo and applies swaps in a single operation, rather than requiring separate runs per face.

  • Batch processing — handles multiple images in one job, useful for processing sets of frames or product shots at volume.

  • Blend strength and color matching controls — lets users fine-tune how the swapped face integrates with the target image's lighting and skin tone.

  • Developer API — a REST interface with webhook callbacks allows the face-swap pipeline to be called programmatically from external services or custom tools.

  • Privacy-focused handling — uploaded and generated files are removed from servers automatically after 24 hours, with no persistent storage of user content.

Use Cases

  • Content creation and social media — creators swap faces onto existing footage or imagery to produce reaction clips, character-based posts, or promotional visuals without hiring actors or reshooting.

  • Meme and comedy content — the platform includes a dedicated "Kirkify" feature for a popular meme format, and the general tool supports similar lightweight creative remixing.

  • Digital art and visual storytelling — artists use face swapping to place a consistent character likeness across a series of generated or stock images.

  • Application development — developers integrate the API to add face-swap functionality to their own products, such as entertainment apps, photo editors, or avatar generators.

  • Video editing and post-production — video editors use the tool to replace a face across a short clip when reshooting is not practical, such as correcting a take or matching a stand-in to a lead actor.

Who Is It For

AI Swap Face is aimed at a broad but digitally active audience. Social media content creators and video editors represent the primary users — people who need fast turnaround on visual effects without access to professional compositing software. Meme creators and hobbyists use the free tier for casual experimentation. App developers and small studios are served by the API and commercial licensing options, which allow the face-swap pipeline to be embedded in products without building underlying models. The multi-language interface (eight languages supported) extends the reach to non-English-speaking markets, and the no-install, browser-based design keeps the barrier to entry low for users who are not technically inclined.

Tools

list_styles

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

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for AI Swap Face.

Input: no parameters. Returns: text/markdown.

get_official_links

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

Input: no parameters. Returns: text/markdown.

Resources

  • site://ai-swap-face/styles — Supported image-generation styles and presets.

  • site://ai-swap-face/pricing — Canonical pricing entry point.

  • site://ai-swap-face/faq — Short FAQ generated from public site metadata.

  • site://ai-swap-face/links — Canonical URLs to share with users.

Prompts

tell_me_about_ai_swap_face

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

try_image_style_ai_swap_face

Recommend a starting image-generation style for a stated goal. — AI Swap Face

Installation

Install via Smithery

npx -y @smithery/cli install ai-swap-face-mcp --client claude

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

Install from source

git clone https://github.com/rocnubie/ai-swap-face-mcp.git
cd ai-swap-face-mcp
pnpm install

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

{
  "mcpServers": {
    "ai-swap-face-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/ai-swap-face-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_pricingA

Return the canonical pricing entry point for AI Swap Face.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, but the tool is simple with zero parameters. The description does not disclose any behavioral traits beyond returning pricing, which is sufficient for a trivial tool.

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?

Single sentence, front-loaded with key action and resource, no unnecessary words.

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 tool with no parameters, no output schema, and simple description, it covers the essential purpose. Sibling tools are listed, providing 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?

No parameters exist, and schema coverage is 100%. The description does not need to add parameter information, but it correctly omits any.

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 'Return' and the specific resource 'canonical pricing entry point for AI Swap Face', which is distinct from sibling tools like list_styles 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 Guidelines2/5

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

No guidance on when to use this tool versus alternatives. No exclusions or context provided.

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

list_stylesB

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

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states 'return' implying read-only, but does not disclose caching, rate limits, or other behavioral traits beyond the basic action.

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?

Single sentence with no waste. However, the parenthetical '(AI Swap Face)' is unclear, slightly reducing clarity.

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 simple no-parameter tool, the description is adequate but lacks detail about the return structure or any examples. Could be more complete.

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?

Input schema has zero parameters, so baseline 4 applies. Description adds no parameter info, which is acceptable as there are none.

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 returns a canonical list of image-generation styles/presets. It distinguishes from sibling tools (get_pricing, get_official_links) by focusing on styles.

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?

No guidance on when to use this tool vs alternatives. The description does not provide context for typical usage or exclusions.

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

A3.6/5.0

Scored across 3 tools

Disambiguation5/5

Each tool serves a distinct informational purpose: listing styles, retrieving pricing, and providing official links. There is no overlap or ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: list_styles, get_pricing, get_official_links. The naming is predictable and uniform.

Tool Count3/5

Three tools is on the low end of the acceptable range for a server claiming to be about face swapping. While not excessive, the number feels minimal for the implied domain.

Completeness1/5

The server name suggests face swapping functionality, but the tools only provide static information (styles, pricing, links). There are no tools to actually perform swaps, upload images, or apply styles, leaving a severe gap in core functionality.

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