AI Vision
Integrates with Google's Gemini and Vertex AI models to analyze images, compare multiple images, and...
1. Using Google AI Studio (Gemini) Provider
If your configuration uses:
IMAGE_PROVIDER=googleVIDEO_PROVIDER=googleGEMINI_API_KEY=your-gemini-api-key
Follow these steps:
Go to Google AI Studio API Keys Page
Visit https://aistudio.google.com/app/api-keys.Create a New API Key
- Click the “Create API Key” button if you do not already have a key.
- Give it a name to help you identify it and select the required scopes if asked.
Copy Your API Key
- Once created, copy your API key. This is the value you'll use for
GEMINI_API_KEY.
- Once created, copy your API key. This is the value you'll use for
Add the Value to FastMCP
- Go to the FastMCP connection interface.
- Use the “Install Now” button or add a new server.
- In the environmental variables section, paste your API key in the
GEMINI_API_KEYfield. Set the following:IMAGE_PROVIDER=googleVIDEO_PROVIDER=googleGEMINI_API_KEY=
2. Using Vertex AI Provider
If your configuration uses:
IMAGE_PROVIDER=vertex_aiVIDEO_PROVIDER=vertex_aiVERTEX_CREDENTIALS=/path/to/service-account.jsonGCS_BUCKET_NAME=your-gcs-bucket
Follow these steps:
Create a Service Account in Google Cloud Console
- Go to Google Cloud Console.
- Navigate to “IAM & Admin” > “Service Accounts”.
- Click “Create Service Account”.
- Give it a name, e.g.,
vertex-ai-mcp.
Grant the Service Account Required Permissions
- Assign the following roles:
- “Vertex AI User”
- “Storage Admin” (so the server can upload to Google Cloud Storage)
- Any other roles your use-case requires.
- Assign the following roles:
Create and Download a Service Account Key
- With your service account selected, go to the “Keys” tab.
- Click “Add Key” > “Create new key”.
- Select “JSON” and click “Create”.
- Download and save the JSON file securely (do not share this file).
Create or Choose a Google Cloud Storage Bucket
- Go to “Storage” in Google Cloud Console.
- Click “Create bucket” or select an existing bucket.
- Note the name of the bucket—you will use this for
GCS_BUCKET_NAME.
Add the Values to FastMCP
- Go to the FastMCP connection interface.
- Use the “Install Now” button or add a new server.
- In the environment variables section:
- Set
IMAGE_PROVIDER=vertex_ai - Set
VIDEO_PROVIDER=vertex_ai - Upload the service account JSON file and set the path as
VERTEX_CREDENTIALS - Set
GCS_BUCKET_NAMEto the bucket name from step 4.
- Set
Note:
Do not expose your Service Account JSON key. Keep it stored securely.
Summary Table:
| Variable | How to obtain |
|---|---|
| GEMINI_API_KEY | Google AI Studio API Keys page |
| VERTEX_CREDENTIALS | Download from Google Cloud “Service Accounts” |
| GCS_BUCKET_NAME | Name from Google Cloud Storage Buckets |
You can now proceed with the rest of your configuration using FastMCP.
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Configuration for
Environment Variables
Please provide values for the following environment variables:
HTTP Headers
Please provide values for the following HTTP headers:
started!
The MCP server should open in . If it doesn't open automatically, please check that you have the application installed.
Copy and run this command in your terminal:
Make sure Gemini CLI is installed:
Visit Gemini CLI documentation for installation instructions.
Make sure Claude Code is installed:
Visit Claude Code documentation for installation instructions.
Installation Steps:
Configuration
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