Amazon Kinesis Video Streams MCP Server
Amazon Kinesis Video Streams is a fully managed AWS service designed for ingesting, storing, and processing video streams at massive scale, enabling developers to build intelligent video applications without managing underlying infrastructure.
Quick Start Summary
The Amazon Kinesis Video Streams MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Kinesis Video Streams API through natural language. It exposes 10 API endpoints as callable tools, such as CreateSignalingChannel, CreateStream, DeleteSignalingChannel, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-kinesisvideo. This integration is sourced from the auto Amazon Kinesis Video Streams OpenAPI specification (v2017-09-30) and has a quality score of 46/99 (fair documentation coverage).
Server Details
- Category
- Design & Creative
- Authentication
- None
- Endpoints
- 10 operations
- Transport
- STDIO
- Spec Version
- v2017-09-30
- Install Command
npx -y @mcp/amazonaws-com-kinesisvideo
Environment Variables
AMAZON_KINESIS_VIDEO_STREAMS_API_KEYExample: your_amazon_kinesis_video_streams_api_key
Top Endpoints
/createSignalingChannelCreateSignalingChannel
/createStreamCreateStream
/deleteSignalingChannelDeleteSignalingChannel
/deleteStreamDeleteStream
/describeEdgeConfigurationDescribeEdgeConfiguration
One-Click Install
Copy the snippet for your MCP client and paste it in — zero editing required.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"amazonaws-com-kinesisvideo": {
"command": "npx",
"args": [
"-y",
"@mcp/amazonaws-com-kinesisvideo"
],
"env": {
"AMAZON_KINESIS_VIDEO_STREAMS_API_KEY": "your_amazon_kinesis_video_streams_api_key"
}
}
}
}Cursor
Settings → MCP Servers → Add
{
"mcpServers": {
"amazonaws-com-kinesisvideo": {
"url": "https://mcpbridge.org/config/amazonaws-com-kinesisvideo.json"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code
Use with MCP extension
{
"mcpServers": {
"amazonaws-com-kinesisvideo": {
"url": "https://mcpbridge.org/config/amazonaws-com-kinesisvideo.json"
}
}
}Endpoints Explorer
Search and browse the 10 operations supported by this server.
Multi-Language Code Examples
Executable code snippets for calling Amazon Kinesis Video Streams endpoints in curl, TypeScript, or Python.
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/kinesisvideo/2017-09-30/createSignalingChannel" \ -H "Content-Type: application/json" \ # No auth required
Manual Configuration
Directly add this block to your JSON config file, or use the hosted config registry URL.
{
"mcpServers": {
"amazonaws-com-kinesisvideo": {
"command": "npx",
"args": ["-y","@mcp/amazonaws-com-kinesisvideo"],
"env": {
"AMAZON_KINESIS_VIDEO_STREAMS_API_KEY": "your_amazon_kinesis_video_streams_api_key"
}
}
}
}Authentication Details
No authentication required. This MCP server runs out-of-the-box.
Documentation Links
Error Handling & HTTP Status Code Matrix
Common status codes, error causes, and resolution steps when invoking Amazon Kinesis Video Streams endpoints.
400 Bad RequestCause: Malformed payload parameters or missing required fields.
Resolution: Verify request schema in Endpoints tab before calling tool.
401 UnauthorizedCause: Missing or invalid API key credentials.
Resolution: Set environment variable in MCP client config under env object.
403 ForbiddenCause: Insufficient scope permissions for requested resource.
Resolution: Verify key permissions in developer dashboard.
404 Not FoundCause: Resource URL path or requested entity ID does not exist.
Resolution: Check path variables and parameters.
429 Rate Limit ExceededCause: API rate limit quota exceeded.
Resolution: Implement exponential backoff retry in tool call.
500 Internal ErrorCause: Upstream server runtime fault.
Resolution: Inspect STDIO stderr output for log trace.
Quality Score
Checked against our protocol-compliance rules.
Specification Version: v2017-09-30
Page compiled on: June 13, 2026
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📖 Detailed MCP Integration Guide
A technical breakdown of capabilities, agent workflows, and security/configuration best practices.
POST /createStream call with appropriate parameters like data retention periods or tags. This integration dramatically reduces the cognitive load and time spent consulting documentation, as the AI possesses real-time, accurate knowledge of the service's capabilities, endpoint structure, and resource relationships, enabling it to suggest optimal configurations or warn about potential misconfigurations during the development process itself.POST /createStream, then generate and configure a dedicated WebRTC signaling channel using POST /createSignalingChannel, and finally describe the new resources using POST /describeSignalingChannel and POST /describeStream to verify their status and provide connection details. Furthermore, the AI can automate maintenance and configuration audits; for instance, a command like "Check all our streams for notification configurations to ensure alerts are enabled" would trigger a sequence of calls to POST /describeNotificationConfiguration across multiple stream ARNs, culminating in a summarized report. The agent can also handle error-driven workflows, such as diagnosing a failed video upload by describing the stream's edge configuration or media storage settings to pinpoint configuration issues.kinesisvideo:CreateStream). The principle of least privilege is paramount; developers should create dedicated IAM roles or users with permissions scoped strictly to necessary actions and specific resource ARNs, avoiding wildcard permissions. When deploying an MCP server that interacts with this API, the credentials should never be hardcoded. Instead, they must be injected via secure environment variables or an IAM role attached to the host environment (like an EC2 instance or ECS task). All communication should occur over TLS, and sensitive parameters, such as those involved in signaling channel access control, must be managed through secure secret management systems rather than exposed in logs or plain-text configuration files.Similar APIs
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Amazon Kinesis Video Streams Media
The Amazon Kinesis Video Streams Media API is a specialized streaming service provided by Amazon Web Services (AWS) that enables developers to reliably ingest, store, and retrieve media streams such as video and audio at scale. The core endpoint, POST /getMedia, facilitates the retrieval of media fragments from Kinesis Video Streams, allowing applications to pull continuous or on-demand video and audio data from cloud-hosted streams. This API is particularly powerful for enterprises and organizations dealing with large volumes of real-time or archival media content originating from cameras, microphones, drones, and other media-producing devices. Typical use cases span security and surveillance systems where footage must be accessed and analyzed on demand, media broadcasting platforms that require low-latency stream retrieval, healthcare applications involving remote patient monitoring video feeds, industrial inspection systems where drones capture inspection footage, and smart city infrastructure that processes live feeds from traffic and public safety cameras. By abstracting the complexity of managing massive media storage and delivery, Kinesis Video Streams Media allows developers to focus on building application logic rather than infrastructure.
Amazon Kinesis Video Streams Archived Media
The Amazon Kinesis Video Streams Archived Media API is a specialized service provided by Amazon Web Services (AWS) that enables programmatic access to retrieve and transform archived video and audio streams stored within Kinesis Video Streams. Its core capabilities center on on-demand data extraction, allowing users to pull specific clips, generate adaptive streaming manifests (HLS and DASH), extract individual image frames, and query the underlying fragment metadata of archived streams. This API is fundamental for enterprises that need to analyze historical video footage, such as for security and surveillance retrospectives, media asset management, industrial IoT inspection, and smart city analytics. Typical use cases include forensic investigation where an operator needs a precise clip of an incident, content creators repurposing raw footage from cloud-based cameras, or automated systems pulling frames for machine learning model training and validation. It serves as the critical data plane for unlocking the value of video data stored in the cloud.
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