Azure Blob Storage MCP Server MCP Server

About

## Azure Blob Storage MCP Server: Cloud Object Storage The **Azure Blob Storage MCP Server** integrates Microsoft Azure Blob Storage into Google Antigravity, enabling blob operations, container management, and shared access signature generation directly from your development environment. ### Why Azure Blob Storage MCP? - **Massive Scale**: Store petabytes of unstructured data with automatic scaling - **Tiered Storage**: Optimize costs with hot, cool, cold, and archive tiers - **Enterprise Security**: Integration with Azure Active Directory and RBAC - **Global Redundancy**: Multiple replication options for high availability - **Native Azure Integration**: Seamless integration with Azure services ### Key Features #### 1. Blob Operations ```python # Upload a blob await mcp.upload_blob( container="my-container", blob_name="documents/report.pdf", file_path="./report.pdf", content_type="application/pdf" ) # Download a blob content = await mcp.download_blob( container="my-container", blob_name="documents/report.pdf" ) # Upload from string/bytes await mcp.upload_blob_data( container="my-container", blob_name="data/config.json", data=json.dumps(config), overwrite=True ) ``` #### 2. Container Management ```python # Create container await mcp.create_container( name="my-container", public_access="blob" # or "container" or None ) # List containers containers = await mcp.list_containers() for container in containers: print(f"Container: {container['name']}") # List blobs in container blobs = await mcp.list_blobs( container="my-container", prefix="documents/" ) for blob in blobs: print(f"Blob: {blob['name']} ({blob['size']} bytes)") ``` #### 3. SAS Token Generation ```python # Generate SAS URL for blob sas_url = await mcp.generate_sas_url( container="my-container", blob_name="documents/report.pdf", permissions="r", # read only expiry_hours=24 ) # Generate container SAS container_sas = await mcp.generate_container_sas( container="uploads", permissions="rwl", # read, write, list expiry_hours=1 ) ``` #### 4. Blob Properties ```python # Get blob properties props = await mcp.get_blob_properties( container="my-container", blob_name="documents/report.pdf" ) print(f"Size: {props['size']}") print(f"Content-Type: {props['content_type']}") print(f"Last Modified: {props['last_modified']}") # Set metadata await mcp.set_blob_metadata( container="my-container", blob_name="documents/report.pdf", metadata={"author": "John", "version": "2"} ) ``` ### Configuration ```json { "mcpServers": { "azure-blob": { "command": "npx", "args": ["-y", "@anthropic/mcp-azure-blob"], "env": { "AZURE_STORAGE_CONNECTION_STRING": "your-connection-string", "AZURE_STORAGE_ACCOUNT_NAME": "your-account", "AZURE_STORAGE_ACCOUNT_KEY": "your-key" } } } } ``` ### Use Cases **Application Storage**: Store user uploads, generated reports, and application assets with secure access. **Archival Storage**: Archive historical data with lifecycle management to optimize storage costs. **Media Streaming**: Store and stream video content with Azure CDN integration for global distribution. **Data Processing**: Stage data for Azure Data Factory, Databricks, or Synapse Analytics pipelines. The Azure Blob Storage MCP enables Azure cloud storage management within your development environment.

Installation

{
  "mcpServers": {
    "azure-blob": {
      "mcpServers": {
        "azure-blob": {
          "env": {
            "AZURE_STORAGE_CONNECTION_STRING": "YOUR_CONNECTION_STRING"
          },
          "args": [
            "-y",
            "azure-blob-storage-mcp-server"
          ],
          "command": "npx"
        }
      }
    }
  }
}

How to Use

1. 1Get connection string from Azure Portal
2. 2Configure storage account access
3. 3Supports container and blob operations