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Soora FR MCP Server

by rocnubie
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Soora FR MCP Server

Soora FR - Generateur d'Images et Videos IA

MCP Badge License: MIT Node MCP

A Model Context Protocol server that exposes the canonical Soora FR 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://soorafr.com

🎨 About Soora FR

Soora FR is a French-language platform for AI video generation, giving francophone creators direct access to a suite of modern video models — including Sora 2, Kling, Veo, Seedance, PixVerse, and Runway — through a single interface. Users describe a scene in text or provide a source image, and the platform produces a short video clip with synchronized audio, realistic physics, and selectable visual styles. The site launched as an accessible alternative to OpenAI's original invitation-only Sora rollout, making the same class of generation available to French-speaking individuals and teams without a waitlist.

Related MCP server: AI Swap Face MCP Server

Key Features

  • Text-to-video and image-to-video generation — describe a scene in a prompt or animate a still image into a moving clip, with typical generation times between 30 and 90 seconds.

  • Multi-model access — switch between Sora 2, Kling, Veo, Seedance, PixVerse, and Runway from one account, choosing the model best suited to a given style or budget.

  • Integrated audio synthesis — generated videos include dialogue, ambient sound, and background music synchronized automatically to the visual content.

  • Storyboard and scene control — chain multiple scenes into a longer sequence using a storyboard tool, with per-scene prompt control.

  • Character Cameo insertion — replace or insert a face into a generated scene, useful for personalized marketing or presenter-style clips.

  • Style and format options — choose from cinematic, photorealistic, anime, or artistic styles; export in 16:9, 9:16, or 1:1 aspect ratios; resolution up to 4K on paid plans.

  • Prompt optimization assistant — a built-in tool refines user-written prompts before sending them to the generation model, improving output consistency.

Use Cases

  • Social media content — produce short-form vertical video for platforms like TikTok, Instagram Reels, or YouTube Shorts without camera equipment or editing software.

  • Product and e-commerce demonstrations — animate a product image or describe a product in use to generate a short showcase clip for a listing or ad.

  • Marketing and advertising — create draft video concepts for campaigns in minutes, then iterate on style, pacing, and messaging before committing to production.

  • Storytelling and filmmaking prototypes — use the storyboard tool to rough out a multi-scene narrative, testing visual tone and scene transitions early in development.

  • Developer integration — access video generation programmatically via the platform's API to embed generation into custom tools or automated content pipelines.

Who Is It For

Soora FR is built primarily for French-speaking creators, marketers, and small teams who want to produce video content without specialized production skills or equipment. It suits social media managers working at volume, e-commerce operators who need product clips quickly, and independent creators who experiment with AI-generated visuals for their audiences. Marketing agencies handling French-language campaigns will find the multi-model selection and commercial licensing on paid plans practical for client work. Developers building content tools in French-speaking markets can integrate the generation API directly. The free tier makes the platform approachable for individuals exploring AI video for the first time, while the Pro plan targets professionals who need longer clips, faster processing, and 4K output.

Tools

list_styles

Return the canonical list of image-generation styles or presets the site exposes. (Soora FR)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for Soora FR.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Soora FR (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

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

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

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

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

Prompts

tell_me_about_soorafr

Summarize what the site is, who it's for, and how it works. — Soora FR

try_image_style_soorafr

Recommend a starting image-generation style for a stated goal. — Soora FR

Installation

Install via Smithery

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

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

Install from source

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

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

{
  "mcpServers": {
    "soorafr-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/soorafr-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 Soora FR.

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 the full burden of behavioral disclosure. It only states that it returns an entry point, without describing the output format, side effects, or any access requirements, leaving significant ambiguity.

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 that immediately conveys the core purpose. It is appropriately concise with no unnecessary information.

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?

Although this is a simple tool with no parameters or output schema, the description does not define what 'pricing entry point' means in terms of the actual return value. This leaves the agent without enough context to fully anticipate the tool's output.

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 baseline score of 4 applies. The description does not need to explain any parameter 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 clearly states the action ('Return') and resource ('canonical pricing entry point'), and the mention of 'Soora FR' differentiates it from sibling tools like list_styles and get_official_links. However, 'entry point' is a bit ambiguous, so it does not achieve 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 Guidelines3/5

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

There is no explicit guidance on when to use this tool versus its siblings. The name and description imply it is for pricing-related queries, but no alternatives or exclusions are mentioned, so the usage context is only implied.

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. (Soora FR)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that the list is 'canonical' and that it represents what the 'site exposes', conveying its authoritative scope. Being a read-only retrieval operation with no side effects, the description provides sufficient transparency without needing to mention auth or rate limits.

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, well-formed sentence that gets straight to the point. The parenthetical '(Soora FR)' adds minimal context without bloating the text. No unnecessary words or repetition.

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?

For a simple, parameterless, read-only list tool with no output schema, the description is complete. It tells the agent exactly what the tool returns (canonical list of styles/presets) and is sufficient for selection. The low complexity means no additional context is needed.

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, which earns a baseline of 4 per the rubric. The description adds no parameter-specific semantics because there are none to explain. The schema already reflects the absence of parameters, so the description need not compensate further.

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: returning a canonical list of image-generation styles/presets. It uses a specific verb ('Return') and resource ('list of image-generation styles or presets'), and the sibling tools (pricing, official links) are obviously distinct. This leaves no ambiguity about what the tool does.

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 implies the usage context: when you need the authoritative list of styles or presets for image generation. It doesn't explicitly state exclusions, but since there are no overlapping sibling tools, the absence of alternatives is clear. For a simple list-retrieval tool, this is adequate.

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

A4.1/5.0

Scored across 3 tools

Disambiguation5/5

Each tool targets a distinct category of information—styles, pricing, and official links—with clear names and descriptions. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case, using either 'list' or 'get' appropriately. This is a coherent and predictable convention.

Tool Count5/5

With only three tools, each serves a specific informational purpose and the set feels appropriately scoped for a lightweight reference server. The count sits within the ideal range and does not feel overly thin or redundant.

Completeness4/5

The server covers the key information areas an agent might need—styles, pricing, and official links—but may lack additional details like per-style specifics or usage policies. Minor gaps exist but core workflows are supported.

Resources

Unclaimed servers have limited discoverability.

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