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AI Video Generator MCP Server

by el-el-san
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Remote

Servidor MCP del generador de video de IA

Este servidor MCP (Protocolo de contexto de modelo) proporciona herramientas para generar videos a partir de indicaciones de texto e imágenes utilizando modelos de generación de imágenes de IA.

Modelos compatibles

  • Luma Ray2 Flash : el modelo de conversión de imagen a vídeo de vanguardia de Luma

  • Kling v1.6 Pro : el modelo de conversión de imágenes a vídeos de alta calidad de Kling

Related MCP server: Video Edit MCP Server

función

  • Generación de vídeo a partir de indicaciones de texto

  • Generación de vídeo con imágenes de inicio y/o fin

  • Controlar los parámetros de vídeo (relación de aspecto, resolución, duración, bucle)

  • Comprobar el estado de la generación

  • Elegir qué modelo de IA utilizar

instalar

  1. Clonar este repositorio

  2. Instalar las dependencias:

    npm install

  3. Cree un archivo .env y configure su clave API FAL.AI:

    FAL_KEY=your_fal_key_here

    Puede obtener la clave API de FAL.AI

Construyendo el servidor

npm run build

Ejecución del servidor

Puedes ejecutar el servidor directamente:

npm start

Integración con Claude Desktop

Para utilizar este servidor con Claude Desktop, agregue lo siguiente a su archivo claude_desktop_config.json :

{
  "mcpServers": {
    "video-generator": {
      "command": "node",
      "args": ["your_install_path/fal-mcp-server/build/index.js"],
      "env": {
        "FAL_KEY": "your_fal_key_here"
      }
    }
  }
}

Herramientas disponibles

generar video

Utiliza modelos de IA para generar vídeos a partir de indicaciones de texto y/o imágenes.

Parámetros:

  • prompt (obligatorio): una descripción de texto del contenido del vídeo que desea generar.

  • image_url (opcional): la URL de la imagen inicial del video (URL o URI de datos base64).

  • end_image_url (opcional): la URL de la imagen final del video (URL o URI de datos base64).

  • aspect_ratio (predeterminado "16:9"): Relación de aspecto del vídeo ("16:9", "9:16", "4:3", "3:4", "21:9", "9:21")

  • resolution (predeterminada "540p"): Resolución de vídeo ("540p", "720p", "1080p")

  • duration (predeterminado "5 s"): duración del vídeo ("5 s", "9 s")

  • loop (predeterminado falso): si el vídeo debe repetirse

  • model (predeterminado "luma"): modelo de IA a utilizar ("luma"=Ray2, "kling"=Kling v1.6 Pro)

comprobar el estado del vídeo

Verifique el estado de su solicitud de generación de vídeo.

Parámetros:

  • request_id (obligatorio): El ID de la solicitud a verificar.

  • model (predeterminado "luma"): modelo de IA utilizado para la solicitud ("luma"=Ray2, "kling"=Kling v1.6 Pro)

Ejemplo de uso de Claude

猫が毛糸玉で遊んでいる動画を生成してください。縦向きモードでお願いします。Klingモデルを使用してください。

Claude llama a la herramienta generate-video con los parámetros apropiados y proporciona la URL del video resultante.

Comparar modelos

  • Flash Luma Ray2 : excelente para movimientos suaves y física realista, produciendo resultados naturales.

  • Kling v1.6 Pro : Excelente para texturas detalladas y efectos especiales, produciendo resultados estilizados.

Dependiendo del mensaje y del resultado deseado, distintos modelos pueden funcionar mejor.

Limitaciones

  • La generación de video puede tardar algún tiempo (especialmente en resoluciones más altas)

  • Se requiere una clave API FAL.AI válida y créditos suficientes

  • Los vídeos de mayor resolución y más largos cuestan más créditos.

  • Ambos modelos consumen créditos FAL.AI (los precios pueden variar según el modelo)

solución de problemas

Error de clave API

Verifique que la variable de entorno FAL_KEY esté configurada correctamente. También se puede configurar directamente en el archivo de configuración de Claude Desktop.

Error de generación de vídeo

Si ocurre un error durante la generación de vídeo, se registrará un mensaje de error detallado. Los problemas comunes son:

  • Clave API no válida o caducada

  • Créditos insuficientes en su cuenta

  • Indicaciones o imágenes inapropiadas

  • Un problema temporal del lado del servidor

Si el error persiste, espere un momento e inténtelo nuevamente o intente cambiar el mensaje.

licencia

Instituto Tecnológico de Massachusetts (MIT)

Available Tools

2 tools
check-video-statusC

Check the status of a video generation request

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoAI model used for the request (luma=Ray2, kling=Kling)luma
request_idYesThe request ID to check

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but reveals nothing about response format, error conditions, rate limits, authentication requirements, or whether this is a read-only operation (though implied by 'check'). For a status-checking tool with zero annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that states the core purpose without any wasted words. It's appropriately sized and front-loaded with the essential information.

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?

For a status-checking tool with no annotations and no output schema, the description is incomplete. It doesn't explain what status information will be returned, possible states (pending, completed, failed), or how to interpret results. The agent would need to guess about the tool's behavior and output format.

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 fully documents both parameters. The description doesn't add any parameter semantics beyond what's in the schema (like explaining the relationship between request_id and previous video generation). Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 ('check') and resource ('video generation request'), making the purpose immediately understandable. However, it doesn't differentiate this status-checking tool from its sibling 'generate-video' tool, which would be helpful for an agent choosing between them.

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. There's no mention of prerequisites (like needing a request_id from a previous generation), nor does it explain the relationship with the sibling 'generate-video' tool, leaving the agent to infer usage context.

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

generate-videoB

Generate a video from text prompt and/or images using AI models (Luma or Kling)

ParametersJSON Schema
NameRequiredDescriptionDefault
aspect_ratioNoAspect ratio of the video16:9
durationNoDuration of the video (9s costs 2x more)5s
end_image_urlNoFinal image to end the video with (URL or base64 data URI)
image_urlNoInitial image to start the video from (URL or base64 data URI)
loopNoWhether the video should loop (blend end with beginning)
modelNoAI model to use (luma=Ray2, kling=Kling)luma
promptYesText description of the desired video content
resolutionNoResolution of the video (higher resolutions use more credits)540p

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions AI models but doesn't disclose important behavioral traits like: whether this is a synchronous or asynchronous operation, what permissions or authentication are needed, rate limits, credit costs (beyond the hint in the duration parameter schema), or what the output looks like. The description is minimal and lacks crucial operational context.

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, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every word earns its place in this concise formulation.

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?

For a complex video generation tool with 8 parameters and no annotations or output schema, the description is insufficient. It doesn't explain the operation's nature (async/sync), authentication requirements, cost implications beyond the duration hint, error conditions, or what happens after invocation. The combination of complexity and lack of structured metadata demands more comprehensive description.

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 all 8 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone, but the description doesn't enhance understanding of parameter usage or relationships.

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 tool's purpose: 'Generate a video from text prompt and/or images using AI models (Luma or Kling)'. It specifies the verb ('generate'), resource ('video'), and input sources ('text prompt and/or images'), but doesn't differentiate from its sibling tool 'check-video-status' beyond the obvious generation vs. status check distinction.

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?

The description implies usage context by mentioning AI models (Luma or Kling), suggesting this is for AI-generated video creation. However, it doesn't provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. The sibling tool 'check-video-status' is clearly complementary rather than an alternative.

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.

  1. 2 tool updatesv1.0.0
    • First observedcheck-video-status
    • First observedgenerate-video
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes with no overlap: one checks status of existing requests, the other creates new videos. An agent would never confuse these functions as they operate on different stages of the video generation workflow.

Naming Consistency5/5

Both tools follow a consistent verb-object naming pattern with hyphen separation: 'check-video-status' and 'generate-video'. The naming is predictable and follows the same convention throughout the set.

Tool Count2/5

With only 2 tools for a video generation server, the surface feels severely limited. While the basic create+status pair covers minimal functionality, a video generation domain typically requires more operations like listing videos, canceling generations, or managing templates. The count is too low for the apparent scope.

Completeness2/5

The toolset provides only generation initiation and status checking, creating significant gaps. Missing are operations like listing existing videos, canceling pending generations, retrieving generated content, managing templates/presets, or configuring generation parameters. Agents will hit dead ends when trying to manage the video lifecycle beyond initial creation.

Maintenance

ActivityInactive
ResponsivenessNo issues

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