Amazon Kinesis Analytics MCP Configuration
The Amazon Kinesis Analytics MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Kinesis Analytics API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including AddApplicationCloudWatchLoggingOption, AddApplicationInput, AddApplicationInputProcessingConfiguration, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the Amazon Kinesis Analytics OpenAPI specification (v2015-08-14) and has a quality score of 46/99 (fair documentation coverage). Use the hosted URL below to auto-load this schema into any compatible MCP client.
Quick Specs Reference
Hosted Config URL
Use this hosted URL in any client that supports remote MCP schema auto-loading.
https://mcpbridge.org/config/amazonaws-com-kinesisanalytics.jsonOne-Click Client Setup
Copy the configurations below to wire your local coding assistant directly.
Claude Desktop
claude_desktop_config.json{
"mcpServers": {
"amazonaws-com-kinesisanalytics": {
"command": "npx",
"args": [
"-y",
"@mcp/amazonaws-com-kinesisanalytics"
],
"env": {
"AMAZON_KINESIS_ANALYTICS_API_KEY": "your_amazon_kinesis_analytics_api_key"
}
}
}
}Cursor & VS Code
MCP Server URL Setup{
"mcpServers": {
"amazonaws-com-kinesisanalytics": {
"url": "https://mcpbridge.org/config/amazonaws-com-kinesisanalytics.json"
}
}
}Raw Configuration JSON
For local command line wrappers or dynamic shell bindings.
{
"mcpServers": {
"amazonaws-com-kinesisanalytics": {
"command": "npx",
"args": ["-y","@mcp/amazonaws-com-kinesisanalytics"],
"env": {
"AMAZON_KINESIS_ANALYTICS_API_KEY": "your_amazon_kinesis_analytics_api_key"
}
}
}
}Required Environment Keys
Substitute these secrets inside your configuration directory environment definitions.
AMAZON_KINESIS_ANALYTICS_API_KEYyour_amazon_kinesis_analytics_api_key with your secret key credentialMapped Web APIs & Tools
The following routes will be exposed directly as protocol tools for the LLM.
/#X-Amz-Target=KinesisAnalytics_20150814.AddApplicationCloudWatchLoggingOptionAddApplicationCloudWatchLoggingOption
/#X-Amz-Target=KinesisAnalytics_20150814.AddApplicationInputAddApplicationInput
/#X-Amz-Target=KinesisAnalytics_20150814.AddApplicationInputProcessingConfigurationAddApplicationInputProcessingConfiguration
/#X-Amz-Target=KinesisAnalytics_20150814.AddApplicationOutputAddApplicationOutput
/#X-Amz-Target=KinesisAnalytics_20150814.AddApplicationReferenceDataSourceAddApplicationReferenceDataSource
/#X-Amz-Target=KinesisAnalytics_20150814.CreateApplicationCreateApplication
/#X-Amz-Target=KinesisAnalytics_20150814.DeleteApplicationDeleteApplication
/#X-Amz-Target=KinesisAnalytics_20150814.DeleteApplicationCloudWatchLoggingOptionDeleteApplicationCloudWatchLoggingOption
/#X-Amz-Target=KinesisAnalytics_20150814.DeleteApplicationInputProcessingConfigurationDeleteApplicationInputProcessingConfiguration
/#X-Amz-Target=KinesisAnalytics_20150814.DeleteApplicationOutputDeleteApplicationOutput
Similar Configurations
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https://mcpbridge.org/config/amazonaws-com-comprehend.jsonAmazon Kinesis Firehose
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https://mcpbridge.org/config/amazonaws-com-firehose.jsonAmazon Kinesis
Amazon Kinesis Data Streams (KDS) is a fully managed, scalable service provided by Amazon Web Services (AWS) designed for real-time ingestion, buffering, and processing of streaming data at massive scale. The Kinesis Data Streams Service API Reference details a comprehensive set of programmatic actions for administering and interacting with Kinesis data streams, which serve as the foundational "plumbing" for real-time data pipelines. Its core capabilities encompass the entire lifecycle of a stream, from creation and configuration to monitoring and deletion. Through these API endpoints, developers and administrators can programmatically create streams with specified shard counts, adjust retention periods for data accessibility, tag streams for cost allocation and organization, manage enhanced monitoring metrics, and control stream consumers for specialized read access. Typical enterprise use cases include real-time application monitoring and log aggregation, live feeds from IoT sensors and devices, real-time analytics on clickstream data, and capturing financial transaction data for immediate processing, fraud detection, or loading into data lakes and warehouses. When exposed as tools via the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop, Cursor, or Cline, the Kinesis API gains a powerful new interaction paradigm that transforms development workflows. The AI agent can dynamically query, manage, and reason about streaming infrastructure as a natural part of a coding or debugging session. This exposure provides immense value by eliminating context-switching and manual console navigation; a developer can instruct the AI to inspect the configuration of a live stream during a code review, verify that monitoring is enabled before deploying a new producer, or even suggest optimal shard count increases based on current usage patterns described in chat. The AI can act as a knowledgeable co-pilot, translating high-level operational intentions into precise API calls, thereby accelerating development, reducing operational errors, and providing instant access to the state of the streaming environment. Practically, a developer could engage the AI agent in several dynamic, context-rich tasks. For instance, one could instruct, "Check the current shard count and retention period for the 'user-activity-stream' and let me know if it aligns with our expected peak load." The AI would use the DescribeStream or DescribeStreamSummary endpoints to retrieve this information and provide an analysis. Another instruction could be, "Set up enhanced monitoring for CPU and iterator age on the 'transaction-stream' so we can debug those lagging consumers," prompting the AI to call the EnableEnhancedMonitoring action. Furthermore, a developer could automate a common administrative workflow by saying, "Create a new stream named 'analytics-pipeline-q4' with 12 shards and set its retention to 168 hours," leading the AI to execute the CreateStream and IncreaseStreamRetentionPeriod calls in sequence, potentially validating the outcome with a subsequent DescribeStream call. Critical attention to security is paramount when exposing such a potent API through an MCP server. The "None" authentication method listed is a placeholder for the actual AWS Signature Version 4 process; in practice, every API request must be cryptographically signed using credentials (access key and secret key) from an IAM (Identity and Access Management) user or role. Adherence to the principle of least privilege is essential: the IAM entity used by the MCP server should be granted only the specific Kinesis actions required for its intended use (e.g., DescribeStream, PutRecord) via a narrowly scoped IAM policy, avoiding broad administrative permissions like "kinesis:*". Developers should also ensure that the MCP server's credentials are stored securely (e.g., not in plain text configuration files) and that all communication occurs over encrypted channels. Furthermore, enabling server-side encryption (SSE) with AWS Key Management Service (KMS) for sensitive streams adds a vital layer of data protection, and implementing VPC endpoints can restrict traffic to the AWS private network, further hardening the security posture.
https://mcpbridge.org/config/amazonaws-com-kinesis.jsonAmazon Mobile Analytics
Amazon Mobile Analytics is a robust, cloud-based service provided by Amazon Web Services (AWS) designed specifically for collecting, processing, visualizing, and analyzing application usage data at scale. This service enables developers and product teams to gain deep, actionable insights into how users interact with their mobile and web applications. At its core, the API exposes a single primary endpoint—a POST request to /2014-06-05/events with an x-amz-Client-Context header—which serves as the ingestion point for event data. Through this endpoint, applications can transmit rich, structured event payloads that capture user sessions, custom events, monetization events, and predefined lifecycle events such as app launch, session start, and session end. Typical enterprise and consumer use cases span from A/B testing analysis and user retention tracking to monetization funnel optimization and crash attribution. Product managers rely on the visual dashboards to monitor daily active users, session lengths, and feature adoption rates, while growth engineers leverage cohort analysis to understand the impact of marketing campaigns. The service is especially valuable in the mobile gaming, e-commerce, and subscription-based application domains, where understanding granular user behavior directly correlates with revenue and engagement outcomes. When this API is exposed as a tool through the Model Context Protocol (MCP) to an AI coding assistant—such as Claude Desktop, Cursor, or Cline—it unlocks a powerful new paradigm of intelligent, context-aware development workflows. The primary value lies in bridging the gap between raw analytics data and developer intent. Rather than requiring a developer to manually query dashboards, export CSV files, or navigate the AWS console to understand user behavior, the AI agent can directly invoke the event ingestion endpoint to programmatically record custom instrumentation events during a debugging or testing session. This means the developer can instruct the AI to emit synthetic user events for integration testing, validate that event schemas are correctly structured before deployment, or batch-test the endpoint's behavior under varying payload conditions. Furthermore, the AI assistant gains awareness of the analytics pipeline, enabling it to suggest schema changes, recommend new event parameters based on observed app usage patterns, and proactively flag missing instrumentation that could lead to blind spots in the data. The MCP integration transforms the analytics service from a passive data sink into an active, queryable intelligence layer that informs the entire software development lifecycle. In practical workflow scenarios, a developer working within an AI-powered IDE could issue commands such as instructing the agent to simulate a complete user onboarding journey by sending a sequence of lifecycle and custom events to the Amazon Mobile Analytics endpoint, thereby validating that the entire funnel is being tracked correctly end-to-end. Another dynamic task might involve asking the AI agent to analyze the event schema documentation and automatically generate type-safe event client libraries in TypeScript or Swift that correctly serialize payloads matching the endpoint's expected structure. The agent could also be tasked with auditing an existing codebase to identify all user interactions that are not currently being instrumented, then generating the corresponding POST requests to the analytics endpoint to fill those gaps. For teams practicing continuous integration, the developer can direct the AI to create automated test scripts that post validation events and confirm successful ingestion responses, ensuring that analytics instrumentation does not regress across releases. These workflows demonstrate how MCP-connected tools empower the AI to move beyond code generation into operational observability and data-driven development. Critical attention to authentication and security best practices is essential when configuring this service for MCP integration. Although the base API endpoint may support various authentication mechanisms, production deployments must enforce AWS Signature Version 4 (SigV4) signing for all requests to ensure request integrity and authenticity. Developers should create dedicated IAM policies following the principle of least privilege, granting only the mobileanalytics:PutEvents permission scoped to the specific resource ARN of the target application. The x-amz-Client-Context header must be carefully constructed to include accurate client metadata—such as app title, version, platform, and locale—to ensure downstream analytics pipelines receive properly contextualized data. It is strongly recommended to avoid hardcoding any AWS credentials in client-side configurations; instead, use AWS Cognito Identity Pools to obtain temporary, short-lived credentials for mobile and web clients. Additionally, developers should implement payload validation on the client side to prevent the accidental transmission of personally identifiable information (PII) or sensitive user data, as analytics events are typically stored in compliance with data retention policies that may not align with strict privacy regulations. Rate limiting, request throttling, and monitoring through Amazon CloudWatch should also be configured to protect the ingestion endpoint from abuse and to maintain service reliability at scale.
https://mcpbridge.org/config/amazonaws-com-mobileanalytics.json