Skip to content
MCP-Bridge
Data & AnalyticsAuto-generatedScore: 28

HDInsightManagementClient MCP Server

The HDInsightManagementClient is a foundational Azure Resource Manager (ARM) API provided by Microsoft, designed to manage and configure Azure HDInsight services.

Quick Start Summary

The HDInsightManagementClient MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the HDInsightManagementClient API through natural language. It exposes 3 API endpoints as callable tools, such as Locations_ListBillingSpecs, Locations_GetCapabilities, Locations_ListUsages. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/azure-com-hdinsight-locations. This integration is sourced from the auto HDInsightManagementClient OpenAPI specification (v2015-03-01-preview) and has a quality score of 28/99 (fair documentation coverage).

3Endpointstools mapped
NoneAuthopen access
28/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Data & Analytics
Authentication
None
Endpoints
3 operations
Transport
STDIO
Spec Version
v2015-03-01-preview
Install Command
npx -y @mcp/azure-com-hdinsight-locations

Environment Variables

HDINSIGHTMANAGEMENTCLIENT_API_KEY

Example: your_hdinsightmanagementclient_api_key

Top Endpoints

GET
/subscriptions/{subscriptionId}/providers/Microsoft.HDInsight/locations/{location}/billingSpecs

Locations_ListBillingSpecs

GET
/subscriptions/{subscriptionId}/providers/Microsoft.HDInsight/locations/{location}/capabilities

Locations_GetCapabilities

GET
/subscriptions/{subscriptionId}/providers/Microsoft.HDInsight/locations/{location}/usages

Locations_ListUsages

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
The HDInsightManagementClient is a foundational Azure Resource Manager (ARM) API provided by Microsoft, designed to manage and configure Azure HDInsight services. HDInsight is a fully managed, full-spectrum open-source analytics service for enterprise data workloads, enabling the deployment and management of clusters for frameworks such as Apache Hadoop, Spark, Hive, LLAP, Kafka, HBase, and Storm. The core capabilities of this specific management client revolve around querying essential administrative and operational metadata at the Azure region level. The provided endpoints allow developers and administrators to retrieve critical planning information: billing specifications for HDInsight services in a given location, the full set of platform capabilities and supported cluster types/features for a region, and the current usage and limits for HDInsight resources within a subscription and location. This API is indispensable for enterprise use cases involving automated infrastructure provisioning, cost analysis, compliance audits, and capacity planning before deploying large-scale data analytics clusters.
🤖AI Agent Value
Exposing this API via tools within an AI coding assistant through the Model Context Protocol (MCP) transforms static infrastructure queries into dynamic, integrated development experiences. Instead of requiring a developer to manually navigate the Azure portal, consult documentation, or run separate CLI commands, the AI assistant gains the ability to access real-time, subscription-specific metadata. This allows the AI to act as a proactive infrastructure advisor and planner. For instance, during the development of a data pipeline, the AI could autonomously verify if the target Azure region supports the required Kafka version and assess the associated billing implications. This integration bridges the gap between application code and underlying cloud resource management, enabling AI agents to make context-aware suggestions, validate deployment prerequisites, and even help forecast costs as part of a code generation or review workflow.
💬Example Workflows
Within an MCP-enabled environment, a developer can instruct the AI agent to perform a series of dynamic, infrastructure-aware tasks. For example, a user could prompt, "Check the available HDInsight capabilities in the 'East US' region for my subscription and compare the billing specs for a Spark cluster versus a Kafka cluster to determine the most cost-effective option for our streaming analytics project." The AI agent, invoking the appropriate MCP tools, would retrieve and synthesize this data, presenting a clear comparative analysis. Another practical workflow could be: "Query the current HDInsight usage in 'West Europe' to see how close we are to our cluster core limits before we script the provisioning of a new development cluster." This automates a manual check, preventing deployment failures. Furthermore, an AI could be tasked with "Generating a deployment plan that lists all supported HDInsight cluster types in 'Southeast Asia' and their key features, to ensure our chosen architecture is compliant with regional platform support."
🛡️Security & Auth
Critical to the secure and effective use of this API is strict adherence to Azure security and authentication practices. Although the prompt notes "None" for authentication in this context, in a real-world implementation, accessing this management API requires proper Azure Active Directory (Azure AD) authentication and authorization. The service principal or user account connecting to the API must be assigned an appropriate Role-Based Access Control (RBAC) role, such as "Reader" or a custom role with the "Microsoft.HDInsight/locations/read" permission, scoped to the relevant subscription or resource group. Following the principle of least privilege is paramount; grant only the permissions necessary for the specific task. Configuration of the MCP server must securely manage Azure credentials, ideally through environment variables or a secrets manager, never hard-coded in source control. Developers should also be aware of potential rate limits on these read operations and implement appropriate throttling or caching in their AI tool integrations to avoid service disruptions.

Similar APIs

Other APIs in the Data & Analytics category.

Amazon Comprehend

Amazon Comprehend is a sophisticated natural language processing (NLP) service provided by Amazon Web Services (AWS) that enables developers to extract meaningful insights and analyze the content of text documents at scale. Its core capabilities extend far beyond basic keyword matching, leveraging pre-trained machine learning models to perform complex linguistic analysis. The service can identify the predominant language, dissect sentiment (positive, negative, neutral, or mixed), recognize named entities such as people, places, and organizations, extract key phrases, and perform syntactic analysis to understand parts of speech and sentence structure. Furthermore, it offers specialized features for detecting and redacting personally identifiable information (PII), classifying documents into custom-defined categories, and analyzing sentiment directed at specific entities within text. This makes it a foundational tool for enterprises needing to process vast volumes of unstructured text data, with use cases ranging from customer review analysis, chatbot intent recognition, and content recommendation engines to compliance monitoring and automated document sorting.

Amazon Kinesis Firehose

Amazon Kinesis Data Firehose is a fully managed service provided by Amazon Web Services (AWS) designed to reliably capture, transform, and load streaming data at scale into AWS data stores and analytics services. Its core capability lies in its ability to handle continuous, high-throughput data streams from millions of sources, including application logs, clickstream data, IoT sensor telemetry, and database change data capture (CDC) streams. The service excels at real-time delivery, allowing users to ingest data and have it routed and delivered to destinations such as Amazon Simple Storage Service (S3) for data lake storage, Amazon OpenSearch Service for real-time log analytics, Amazon Redshift for near real-time business intelligence dashboards, and third-party platforms like Splunk for operational monitoring. 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.

Amazon 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.

Amazon 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.

Related MCP Server Integrations

Amazon Comprehend MCP Setup

Amazon Comprehend is a sophisticated natural language processing (NLP) service provided by Amazon Web Services (AWS) that enables developers to extract meaningful insights and analyze the content of text documents at scale. Its core capabilities extend far beyond basic keyword matching, leveraging pre-trained machine learning models to perform complex linguistic analysis. The service can identify the predominant language, dissect sentiment (positive, negative, neutral, or mixed), recognize named entities such as people, places, and organizations, extract key phrases, and perform syntactic analysis to understand parts of speech and sentence structure. Furthermore, it offers specialized features for detecting and redacting personally identifiable information (PII), classifying documents into custom-defined categories, and analyzing sentiment directed at specific entities within text. This makes it a foundational tool for enterprises needing to process vast volumes of unstructured text data, with use cases ranging from customer review analysis, chatbot intent recognition, and content recommendation engines to compliance monitoring and automated document sorting.

Data & AnalyticsConfigure →

Amazon Kinesis Firehose MCP Setup

Amazon Kinesis Data Firehose is a fully managed service provided by Amazon Web Services (AWS) designed to reliably capture, transform, and load streaming data at scale into AWS data stores and analytics services. Its core capability lies in its ability to handle continuous, high-throughput data streams from millions of sources, including application logs, clickstream data, IoT sensor telemetry, and database change data capture (CDC) streams. The service excels at real-time delivery, allowing users to ingest data and have it routed and delivered to destinations such as Amazon Simple Storage Service (S3) for data lake storage, Amazon OpenSearch Service for real-time log analytics, Amazon Redshift for near real-time business intelligence dashboards, and third-party platforms like Splunk for operational monitoring. 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.

Data & AnalyticsConfigure →

Amazon Kinesis MCP Setup

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.

Data & AnalyticsConfigure →

Amazon Kinesis Analytics MCP Setup

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.

Data & AnalyticsConfigure →

Amazon Mobile Analytics MCP Setup

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.

Data & AnalyticsConfigure →