The MCP Video Generation with Veo2 server allows users to generate and manage videos and images using Google's Veo2 and Imagen models. You can:
Generate videos from text prompts with configurable options like aspect ratio, duration, and person generation settings
Generate videos from images (provided as base64 data, URI, or ID), optionally guided by text prompts
Generate images from text prompts using Google Imagen, specifying the number of images desired
Generate video from generated image in a single step, using separate prompts for image and video creation
List generated videos and images that have been previously created
Retrieve specific generated images by their unique ID
Integrates with Google's Veo2 video generation capabilities, allowing generation of videos from text prompts or images with various configuration options such as aspect ratio, duration, and person generation settings.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Video Generation with Veo2 generate a cinematic video of a sunset over mountains with golden hour lighting"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This project implements a Model Context Protocol (MCP) server that exposes Google's Veo2 video generation capabilities. It allows clients to generate videos from text prompts or images, and access the generated videos through MCP resources.
Generate videos from text prompts
Generate videos from images
Access generated videos through MCP resources
Example video generation templates
Support for both stdio and SSE transports
Related MCP server: hyper-video-service
Image to Video - from Grok generated puppy
Image to Video - from real cat
Node.js 18 or higher
Google API key with access to Gemini API and Veo2 model (= You need to set up a credit card with your API key! -> Go to aistudio.google.com )
Click Add Server
Copy & Paste Github URL into FLUJO
Click Parse, Clone, Install, Build and Save.
To install mcp-video-generation-veo2 for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @mario-andreschak/mcp-veo2 --client claudeClone the repository:
git clone https://github.com/yourusername/mcp-video-generation-veo2.git
cd mcp-video-generation-veo2Install dependencies:
npm installCreate a .env file with your Google API key:
cp .env.example .env
# Edit .env and add your Google API keyThe .env file supports the following variables:
GOOGLE_API_KEY: Your Google API key (required)
PORT: Server port (default: 3000)
STORAGE_DIR: Directory for storing generated videos (default: ./generated-videos)
LOG_LEVEL: Logging level (default: fatal)
Available levels: verbose, debug, info, warn, error, fatal, none
For development, set to debug or info for more detailed logs
For production, keep as fatal to minimize console output
Build the project:
npm run buildYou can start the server with either stdio or SSE transport:
npm start
# or
npm start stdionpm start sseThis will start the server on port 3000 (or the port specified in your .env file).
The server exposes the following MCP tools:
Generates a video from a text prompt.
Parameters:
prompt (string): The text prompt for video generation
config (object, optional): Configuration options
aspectRatio (string, optional): "16:9" or "9:16"
personGeneration (string, optional): "dont_allow" or "allow_adult"
numberOfVideos (number, optional): 1 or 2
durationSeconds (number, optional): Between 5 and 8
enhancePrompt (boolean, optional): Whether to enhance the prompt
negativePrompt (string, optional): Text describing what not to generate
Example:
{
"prompt": "Panning wide shot of a serene forest with sunlight filtering through the trees, cinematic quality",
"config": {
"aspectRatio": "16:9",
"personGeneration": "dont_allow",
"durationSeconds": 8
}
}Generates a video from an image.
Parameters:
image (string): Base64-encoded image data
prompt (string, optional): Text prompt to guide the video generation
config (object, optional): Configuration options (same as above, but personGeneration only supports "dont_allow")
Lists all generated videos.
The server exposes the following MCP resources:
Access a generated video by its ID.
Access example video generation templates.
src/: Source code
index.ts: Main entry point
server.ts: MCP server configuration
config.ts: Configuration handling
tools/: MCP tool implementations
resources/: MCP resource implementations
services/: External service integrations
utils/: Utility functions
npm run buildnpm run devMIT
Generate an image from a text prompt using Google Imagen
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| numberOfImages | No | ||
| includeFullData | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not mention side effects, authentication needs, rate limits, or whether the image is stored or returned directly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise. However, it sacrifices valuable information for brevity. Not every sentence earns its place when it omits critical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain return values or parameter behavior, leaving the agent without enough context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description adds no meaning beyond the parameter names and types in the input schema. It fails to explain prompt constraints, the number of images, or the includeFullData field.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Generate'), the resource ('an image'), and the input ('from a text prompt using Google Imagen'). It effectively distinguishes from sibling tools focused on video generation or listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 like getImage or listGeneratedImages. The description does not mention prerequisites, exclusions, or context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a video from a generated image (one-step process)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| aspectRatio | No | 16:9 | |
| videoPrompt | No | ||
| autoDownload | No | ||
| enhancePrompt | No | ||
| negativePrompt | No | ||
| numberOfImages | No | ||
| numberOfVideos | No | ||
| durationSeconds | No | ||
| includeFullData | No | ||
| personGeneration | No | dont_allow |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description only mentions 'one-step process'. Important behavioral traits such as cost, async behavior, output format, and side effects are not disclosed. The description fails to compensate for the lack of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is too brief given the tool's complexity (11 parameters). It is under-specified and does not effectively convey necessary information despite being concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal given the large parameter count and lack of output schema. It does not explain the overall workflow (e.g., whether the image is generated first or input is needed) or how the tool fits with siblings. Important context like return values and constraints is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain parameters but does not. None of the 11 parameters are described in the description, and the schema itself lacks descriptions. The agent cannot understand what parameters like 'autoDownload' or 'personGeneration' do.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action (generate video) and the source (generated image). However, it does not differentiate from the sibling tool 'generateVideoFromImage', which could lead to confusion about when to use this tool vs that one.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like generateVideoFromImage or generateVideoFromText. There is no explanation of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a video from an image
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | ||
| prompt | No | Generate a video from this image | |
| aspectRatio | No | 16:9 | |
| autoDownload | No | ||
| enhancePrompt | No | ||
| negativePrompt | No | ||
| numberOfVideos | No | ||
| durationSeconds | No | ||
| includeFullData | No | ||
| personGeneration | No | dont_allow |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states the basic operation. It does not disclose any behavioral traits such as processing time, image format requirements, or potential limitations, leaving the agent without important context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is under-specified for a tool with 10 parameters. It lacks structure and important details, making it minimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, no output schema, no annotations), the description is grossly incomplete. It fails to provide context for the output, parameter behavior, or usage scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero description coverage for parameters, and the description adds no meaning about any of the 10 parameters. Parameters like 'prompt', 'aspectRatio', 'durationSeconds' are not explained, so the agent cannot infer their purpose from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a video from an image, using a specific verb and resource. However, it does not differentiate from sibling tool 'generateVideoFromGeneratedImage', which has a similar purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have an image to convert to a video, but provides no explicit guidance on when to use this tool versus alternatives like 'generateVideoFromText' or 'generateVideoFromGeneratedImage'. No exclusions or context are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a video from a text prompt
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| aspectRatio | No | 16:9 | |
| autoDownload | No | ||
| enhancePrompt | No | ||
| negativePrompt | No | ||
| numberOfVideos | No | ||
| durationSeconds | No | ||
| includeFullData | No | ||
| personGeneration | No | dont_allow |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral transparency. It only states the basic function without disclosing any behavioral traits such as generation time, cost, success/failure handling, or return format. This is insufficient for responsible agent use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at one sentence, but given the tool's complexity (9 parameters), it is too brief. While front-loaded with the core action, it sacrifices necessary detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Considering the tool's complexity (9 parameters, no output schema, no annotations), the description is critically incomplete. It lacks details on output, behavior, parameter effects, and usage context, making it inadequate for correct agent selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description adds no value by explaining parameters like enhancePrompt, autoDownload, includeFullData, or personGeneration. Agents must infer meaning from names alone, risking misuse. At minimum, key parameters should be explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate a video from a text prompt' clearly states the verb (Generate) and resource (video from text), distinguishing it from siblings like generateImage (image from text) and generateVideoFromImage (video from existing image). It's specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives. It doesn't mention scenarios, prerequisites, or exclusions, leaving the agent without context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a specific image by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| includeFullData | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose behavioral traits beyond the basic operation. It lacks details about error handling, response format, or side effects. With no annotations, the description carries the full burden but provides minimal insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence. Every word is necessary and there is no wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, no parameter descriptions in the schema, and no annotations, the description is insufficient. It does not explain return values, error states, or the effect of optional parameters, leaving significant gaps for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description only hints at the 'id' parameter through the phrase 'by ID', but does not explain the 'includeFullData' parameter. Schema description coverage is 0%, so the description should compensate but does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get a specific image by ID' clearly states the verb (Get), resource (specific image), and method (by ID). It effectively distinguishes from sibling tools like generateImage, which are generative in nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it does not clarify that this tool is for retrieving existing images, while siblings handle generation or listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all generated images
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose read-only nature, auth requirements, or any side effects. Simply states 'list all', offering no behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise at one sentence, no wasted words. Could be slightly improved by front-loading key info, but currently adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple list tool with no params or output schema; however, lacks any mention of pagination or ordering, which may be necessary for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters; schema coverage is 100%. Description adds no parameter info, but none is needed. Baseline 4 is appropriate for no-param tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb (list) and resource (generated images), distinguishing from siblings like getImage (single) and generateImage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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; no mention of filtering, pagination, or context such as if the list is all images or scoped to a user.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all generated videos
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description merely repeats the tool name without disclosing any behavioral traits (e.g., pagination, scope of 'all', or read-only nature).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, efficient sentence with no extraneous words. Appropriate for a simple list tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list tool, the description is acceptable but lacks details like result format, sorting, or limits. Could be improved with a note on scope or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema is empty), so the description has no parameter semantics to add. Baseline 4 applies as the schema already covers 100% of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List all generated videos' clearly states the verb (List) and resource (generated videos), distinguishing it from siblings like generateImage, generateVideoFromText, and listGeneratedImages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 such as getImage or generateVideoFromText. The description does not specify context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes, but generateVideoFromGeneratedImage and generateVideoFromImage overlap in functionality; one is described as 'one-step process' but the distinction is not immediately clear from names alone.
Tool names follow a verb_noun pattern, but there is inconsistency: 'generateImage' uses a direct object while video tools use 'from' prepositional phrases. Also, 'getImage' differs from 'listGeneratedImages/listGeneratedVideos' in tense.
With 7 tools, the set is well-scoped for an image/video generation server, covering creation and listing without being overly numerous.
The tool surface includes generation and listing but lacks retrieval of individual videos (no getVideo), update, and delete operations, leaving notable gaps in lifecycle management.
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