Amazon Comprehend MCP Configuration
The Amazon Comprehend MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Comprehend API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including BatchDetectDominantLanguage, BatchDetectEntities, BatchDetectKeyPhrases, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the Amazon Comprehend OpenAPI specification (v2017-11-27) 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-comprehend.jsonOne-Click Client Setup
Copy the configurations below to wire your local coding assistant directly.
Claude Desktop
claude_desktop_config.json{
"mcpServers": {
"amazonaws-com-comprehend": {
"command": "npx",
"args": [
"-y",
"@mcp/amazonaws-com-comprehend"
],
"env": {
"AMAZON_COMPREHEND_API_KEY": "your_amazon_comprehend_api_key"
}
}
}
}Cursor & VS Code
MCP Server URL Setup{
"mcpServers": {
"amazonaws-com-comprehend": {
"url": "https://mcpbridge.org/config/amazonaws-com-comprehend.json"
}
}
}Raw Configuration JSON
For local command line wrappers or dynamic shell bindings.
{
"mcpServers": {
"amazonaws-com-comprehend": {
"command": "npx",
"args": ["-y","@mcp/amazonaws-com-comprehend"],
"env": {
"AMAZON_COMPREHEND_API_KEY": "your_amazon_comprehend_api_key"
}
}
}
}Required Environment Keys
Substitute these secrets inside your configuration directory environment definitions.
AMAZON_COMPREHEND_API_KEYyour_amazon_comprehend_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=Comprehend_20171127.BatchDetectDominantLanguageBatchDetectDominantLanguage
/#X-Amz-Target=Comprehend_20171127.BatchDetectEntitiesBatchDetectEntities
/#X-Amz-Target=Comprehend_20171127.BatchDetectKeyPhrasesBatchDetectKeyPhrases
/#X-Amz-Target=Comprehend_20171127.BatchDetectSentimentBatchDetectSentiment
/#X-Amz-Target=Comprehend_20171127.BatchDetectSyntaxBatchDetectSyntax
/#X-Amz-Target=Comprehend_20171127.BatchDetectTargetedSentimentBatchDetectTargetedSentiment
/#X-Amz-Target=Comprehend_20171127.ClassifyDocumentClassifyDocument
/#X-Amz-Target=Comprehend_20171127.ContainsPiiEntitiesContainsPiiEntities
/#X-Amz-Target=Comprehend_20171127.CreateDatasetCreateDataset
/#X-Amz-Target=Comprehend_20171127.CreateDocumentClassifierCreateDocumentClassifier
Similar Configurations
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Typical enterprise use cases involve building foundational data pipelines for big data analytics, enabling real-time security event monitoring, implementing centralized logging for distributed applications, and powering live dashboards that require sub-second data freshness. It removes the operational burden of managing infrastructure and software for streaming data ingestion, offering features like automatic scaling, data transformation with AWS Lambda, and flexible data backup mechanisms. When exposed as tooling via the Model Context Protocol (MCP) to an AI coding assistant, the Kinesis Data Firehose API provides a powerful interface for dynamic, automated data pipeline management. An AI agent becomes a programmable operator capable of orchestrating the lifecycle of streaming data flows. The value lies in the ability to translate high-level, natural language instructions into precise API operations, dramatically accelerating development and operational workflows. For instance, a developer can instruct the AI to "set up a new delivery stream to route application error logs to S3 with a 5-minute buffering interval and enable GZIP compression," and the AI can construct and execute the `CreateDeliveryStream` call with the appropriate configuration. Similarly, an AI could be tasked with "listing all delivery streams that are currently encrypted," using the `ListDeliveryStreams` and `DescribeDeliveryStreams` endpoints to audit compliance. This turns the AI assistant into a collaborative partner for real-time data architecture, capable of implementing complex configurations, diagnosing stream health issues, and performing routine maintenance tasks on behalf of the developer. In practice, a developer working with an MCP server for Kinesis Firehose can engage in a variety of dynamic, automated workflows. 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Furthermore, tasks like enabling server-side encryption with a new AWS Key Management Service (KMS) key for a specific stream, tagging streams for cost allocation, or temporarily stopping a stream for maintenance are all operations that can be precisely orchestrated through natural language instructions. Security and authentication are paramount when configuring an MCP server for this API. While the API reference itself notes "None" for a specific method, all actual requests to the AWS API must be authenticated using valid AWS credentials, typically an IAM role or user with an access key ID and secret access key. The critical best practice is to apply the principle of least privilege: create a dedicated IAM policy that grants only the specific Firehose permissions required for the intended tasks (e.g., `firehose:DescribeDeliveryStream`, `firehose:PutRecord`) on the specific stream resources (`Resource: "arn:aws:firehose:region:account-id:deliverystream/stream-name"`). Developers must ensure these credentials are securely managed and never embedded in client-side code or exposed in logs. Additional configuration guidelines include enabling server-side encryption with a customer-managed KMS key for all streams handling sensitive data, utilizing VPC endpoints to keep traffic on the AWS network, and configuring data transformation functions with appropriate IAM roles that follow least privilege principles. It is also advisable to set up robust monitoring and alerting on stream metrics like `DeliveryToDestinationSuccess` and `IncomingBytes` to ensure operational health.
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 Kinesis Analytics
Amazon Kinesis Analytics (version 1) is a managed service provided by Amazon Web Services (AWS) that enables developers to query and analyze streaming data in real time using standard SQL. The API serves as the programmatic interface for creating, configuring, and managing analytics applications that continuously process and analyze data from streaming sources such as Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose. Core capabilities include creating and deleting applications, defining and modifying input sources, configuring output destinations, adding reference data for enrichment, and setting up logging to Amazon CloudWatch for monitoring and debugging. This service is foundational for enterprise use cases requiring real-time operational intelligence, such as fraud detection in financial transactions, live monitoring of IT infrastructure logs, real-time analytics on clickstream data for e-commerce personalization, and operational dashboards that visualize system health metrics as they occur. It transforms raw streaming data into actionable insights with minimal latency, reducing the need for complex batch processing pipelines. When exposed as a toolset via the Model Context Protocol (MCP) to an AI coding assistant, the Amazon Kinesis Analytics API gains significant contextual value. The AI agent can act as a highly efficient operations and development partner, directly manipulating the lifecycle of analytics applications. Instead of a developer manually writing AWS CLI commands or navigating the AWS Management Console, they can issue natural language instructions. The AI, with access to these endpoints, can interpret intent and execute precise API calls to perform tasks like programmatically provisioning a new analytics application for a specific data stream, dynamically adjusting the input processing configuration to handle data format changes, or scaling output resources in response to detected throughput issues. This integration automates routine DevOps tasks, accelerates development cycles, and reduces the cognitive load on engineers, allowing them to focus on higher-level application logic and data modeling rather than infrastructure management. For a practical workflow, consider a scenario where a developer needs to set up a new real-time analytics pipeline for log data. The developer can instruct the AI agent: "Create a new Kinesis Analytics application named 'LogAnalyzer' that ingests data from my Kinesis stream 'app-logs-stream'." The AI would use the CreateApplication and AddApplicationInput endpoints to build and configure the foundation. Subsequently, the developer can refine the pipeline with commands like, "Update the 'LogAnalyzer' application to use a reference data file from S3 to enrich the incoming logs with geo-location data," prompting the AI to call AddApplicationReferenceDataSource. To redirect the analyzed output for archiving, the developer might say, "Send the output from 'LogAnalyzer' to a new Firehose delivery stream for long-term storage," which would trigger an AddApplicationOutput call. This conversational orchestration of the API endpoints enables rapid prototyping and agile modification of real-time data workflows. Critical security and configuration practices must be enforced when setting up an MCP server for this API. Although the API itself relies on AWS Identity and Access Management (IAM) for authentication, the connection between the AI assistant and the API endpoints must be secured. Developers must never hardcode AWS credentials. Instead, the MCP server should be configured to use an IAM role with the principle of least privilege, granting only the specific Kinesis Analytics permissions (e.g., kinesisanalytics:CreateApplication, kinesisanalytics:AddApplicationInput) required for the intended tasks. All API calls should be routed over HTTPS. For enhanced security, the Kinesis Analytics application itself should be configured within a Virtual Private Cloud (VPC) to control network access to its underlying resources. Furthermore, developers should implement thorough error handling in the AI's interaction logic and maintain audit logs of all automated changes to ensure traceability and compliance with operational governance policies.
https://mcpbridge.org/config/amazonaws-com-kinesisanalytics.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