[
https://issues.apache.org/jira/browse/AMBARI-26532?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nikita Pande updated AMBARI-26532:
----------------------------------
Description:
Integrating Ambari with MCP is not merely a technical exercise; it unlocks a
new paradigm of cluster management, shifting from manual, UI-driven operations
to conversational, automated, and ultimately autonomous control. This
transformation enables a range of high-value use cases that can dramatically
reduce operational overhead and democratize administrative expertise.
* *Natural Language Diagnostics & Troubleshooting:* This is the most immediate
and compelling use case. Administrators, regardless of their expertise level,
can interact with the cluster in plain English to diagnose issues. Instead of
navigating through multiple screens in the Ambari UI or crafting complex
{{curl}} commands, they can simply ask questions. For instance:
** _"Why did the HDFS service health check fail on node '<nodeName>?"_
** _"Show me all CRITICAL alerts from the last 24 hours related to YARN."_
** _"What is the current heap usage of the NameNode, and how does it compare
to yesterday?"_ To answer these, an AI agent would leverage MCP {{Resources}}
to fetch health reports, alert histories, and performance metrics from Ambari,
then use its reasoning capabilities to synthesize a coherent, human-readable
answer.
* *Automated and Agentic Remediation:* Moving beyond diagnosis, this
integration empowers AI agents to take corrective actions. This creates a
"self-healing" capability for the cluster. An agent can be instructed to
execute complex remediation workflows that involve a chain of actions and
checks. For example:
** _"The NameNode is in standby. Investigate the logs for critical errors. If
none are found within the last 15 minutes, attempt a restart and confirm it
becomes active. Notify the support channel in chat interface with the result."_
This workflow would require the agent to chain multiple MCP {{Tool}} calls: get
logs ({{{}Resource{}}}), analyze them (LLM reasoning), restart the service
({{{}Tool{}}}), and check its status ({{{}Resource{}}}), demonstrating a
sophisticated, agentic process.
* *Conversational Configuration and Security Audits:* Complex configuration
changes and security hardening are often error-prone. A conversational
interface simplifies these tasks significantly.
** _"Increase the YARN NodeManager memory to 32GB on all worker nodes and then
perform a rolling restart of the YARN service."_
*
** _"Audit the cluster for security compliance. List all services that do not
have Kerberos enabled and generate the sequence of API calls required to
configure them."_ These commands would be translated by the agent into a series
of {{updateServiceConfig}} and {{restartService}} tool calls, executed in the
correct order.
* *Declarative Provisioning via Conversation:* This use case represents an
evolution of Ambari Blueprints, making cluster provisioning more accessible. An
administrator could describe the desired cluster in high-level terms, and the
AI agent would handle the low-level details of creating the Blueprint JSON.
** _"Provision a new 5-node test cluster using <stack name and version>. The
cluster should include HDFS, YARN, and Spark. Designate 'master01' as the
master node with the NameNode and ResourceManager, and the rest as worker nodes
with DataNodes and NodeManagers."_ The agent would parse this request, generate
the corresponding Blueprint, and use an MCP {{Tool}} to submit it to the Ambari
API, initiating the cluster deployment.
* *Proposed Solution:* This feature proposes the development and integration
of a new, standalone {*}Ambari MCP Server{*}. This service will expose Ambari's
rich management capabilities through the open and rapidly-adopted Model Context
Protocol (MCP). By doing so, it will allow any MCP-compatible AI agent or host
application (e.g., VS Code with Copilot, Claude Desktop) to securely discover
and interact with the Ambari-managed cluster. The server will map Ambari's REST
API endpoints to MCP's core primitives: state-changing operations will be
exposed as {{{}Tools{}}}, read-only data queries as {{{}Resources{}}}, and
complex, multi-step administrative tasks as {{{}Prompts{}}}. This will
effectively transform Ambari from a passive management tool into an active,
intelligent platform accessible via natural language and agentic workflows.
*Key Benefits:*
*Reduced Operational Overhead:* Enable administrators to diagnose issues,
perform restarts, and modify configurations using simple, conversational
commands, automating routine tasks.
*Democratized Expertise:* Allow less experienced operators to perform complex
administrative operations safely by leveraging pre-defined, reliable MCP
Prompts that encapsulate expert workflows.
*Enhanced Automation and Self-Healing:* Provide the foundation for building
sophisticated, agentic systems that can proactively monitor cluster health,
diagnose failures, and execute remediation plans autonomously.
*Ecosystem Interoperability:* Position Ambari as a first-class citizen in the
burgeoning ecosystem of AI development tools and agentic frameworks by adopting
the MCP standard, ensuring its future relevance.
*Roadmap:*
*
** Read-Only Integration (The Observer) - Phase 1: Exposing all relevant
cluster state, including service statuses, host information, component layouts,
configurations, alert histories, and performance metrics.
** Actionable Tools (The Operator) - Phase 2: Enable direct, conversational
control over the cluster. Administrators can now use the AI agent as a remote
control for Ambari, issuing commands to operate the cluster.
** Abstracted Workflows (The Autonomous Agent) - Phase 3: Achieve true agentic
behavior. This phase moves beyond simple command-and-control to a state where
the AI can be delegated complex, long-running tasks, executing sophisticated
strategies with minimal human intervention and unlocking the full potential of
autonomous data platform management.
Refer [https://modelcontextprotocol.io/]
was:
Integrating Ambari with MCP is not merely a technical exercise; it unlocks a
new paradigm of cluster management, shifting from manual, UI-driven operations
to conversational, automated, and ultimately autonomous control. This
transformation enables a range of high-value use cases that can dramatically
reduce operational overhead and democratize administrative expertise.
* *Natural Language Diagnostics & Troubleshooting:* This is the most immediate
and compelling use case. Administrators, regardless of their expertise level,
can interact with the cluster in plain English to diagnose issues. Instead of
navigating through multiple screens in the Ambari UI or crafting complex
{{curl}} commands, they can simply ask questions. For instance:
** _"Why did the HDFS service health check fail on node '<nodeName>?"_
** _"Show me all CRITICAL alerts from the last 24 hours related to YARN."_
** _"What is the current heap usage of the NameNode, and how does it compare
to yesterday?"_ To answer these, an AI agent would leverage MCP {{Resources}}
to fetch health reports, alert histories, and performance metrics from Ambari,
then use its reasoning capabilities to synthesize a coherent, human-readable
answer.
* *Automated and Agentic Remediation:* Moving beyond diagnosis, this
integration empowers AI agents to take corrective actions. This creates a
"self-healing" capability for the cluster. An agent can be instructed to
execute complex remediation workflows that involve a chain of actions and
checks. For example:
** _"The NameNode is in standby. Investigate the logs for critical errors. If
none are found within the last 15 minutes, attempt a restart and confirm it
becomes active. Notify the support channel in chat interface with the result."_
This workflow would require the agent to chain multiple MCP {{Tool}} calls: get
logs ({{{}Resource{}}}), analyze them (LLM reasoning), restart the service
({{{}Tool{}}}), and check its status ({{{}Resource{}}}), demonstrating a
sophisticated, agentic process.
* *Conversational Configuration and Security Audits:* Complex configuration
changes and security hardening are often error-prone. A conversational
interface simplifies these tasks significantly.
** _"Increase the YARN NodeManager memory to 32GB on all worker nodes and then
perform a rolling restart of the YARN service."_
*
** _"Audit the cluster for security compliance. List all services that do not
have Kerberos enabled and generate the sequence of API calls required to
configure them."_ These commands would be translated by the agent into a series
of {{updateServiceConfig}} and {{restartService}} tool calls, executed in the
correct order.
* *Declarative Provisioning via Conversation:* This use case represents an
evolution of Ambari Blueprints, making cluster provisioning more accessible. An
administrator could describe the desired cluster in high-level terms, and the
AI agent would handle the low-level details of creating the Blueprint JSON.
** _"Provision a new 5-node test cluster using <stack name and version>. The
cluster should include HDFS, YARN, and Spark. Designate 'master01' as the
master node with the NameNode and ResourceManager, and the rest as worker nodes
with DataNodes and NodeManagers."_ The agent would parse this request, generate
the corresponding Blueprint, and use an MCP {{Tool}} to submit it to the Ambari
API, initiating the cluster deployment.
* *Proposed Solution:* This feature proposes the development and integration
of a new, standalone {*}Ambari MCP Server{*}. This service will expose Ambari's
rich management capabilities through the open and rapidly-adopted Model Context
Protocol (MCP). By doing so, it will allow any MCP-compatible AI agent or host
application (e.g., VS Code with Copilot, Claude Desktop) to securely discover
and interact with the Ambari-managed cluster. The server will map Ambari's REST
API endpoints to MCP's core primitives: state-changing operations will be
exposed as {{{}Tools{}}}, read-only data queries as {{{}Resources{}}}, and
complex, multi-step administrative tasks as {{{}Prompts{}}}. This will
effectively transform Ambari from a passive management tool into an active,
intelligent platform accessible via natural language and agentic workflows.
*Key Benefits:*
*Reduced Operational Overhead:* Enable administrators to diagnose issues,
perform restarts, and modify configurations using simple, conversational
commands, automating routine tasks.
*Democratized Expertise:* Allow less experienced operators to perform complex
administrative operations safely by leveraging pre-defined, reliable MCP
Prompts that encapsulate expert workflows.
*Enhanced Automation and Self-Healing:* Provide the foundation for building
sophisticated, agentic systems that can proactively monitor cluster health,
diagnose failures, and execute remediation plans autonomously.
*Ecosystem Interoperability:* Position Ambari as a first-class citizen in the
burgeoning ecosystem of AI development tools and agentic frameworks by adopting
the MCP standard, ensuring its future relevance.
*Roadmap:*
*
** Read-Only Integration (The Observer) - Phase 1: Exposing all relevant
cluster state, including service statuses, host information, component layouts,
configurations, alert histories, and performance metrics.
** Actionable Tools (The Operator) - Phase 2: Enable direct, conversational
control over the cluster. Administrators can now use the AI agent as a remote
control for Ambari, issuing commands to operate the cluster.
** Abstracted Workflows (The Autonomous Agent) - Phase 3: Achieve true agentic
behavior. This phase moves beyond simple command-and-control to a state where
the AI can be delegated complex, long-running tasks, executing sophisticated
strategies with minimal human intervention and unlocking the full potential of
autonomous data platform management.
> Add Model Context Protocol (MCP) Server for AI-Driven Cluster Management
> ------------------------------------------------------------------------
>
> Key: AMBARI-26532
> URL: https://issues.apache.org/jira/browse/AMBARI-26532
> Project: Ambari
> Issue Type: New Feature
> Reporter: Nikita Pande
> Priority: Major
>
> Integrating Ambari with MCP is not merely a technical exercise; it unlocks a
> new paradigm of cluster management, shifting from manual, UI-driven
> operations to conversational, automated, and ultimately autonomous control.
> This transformation enables a range of high-value use cases that can
> dramatically reduce operational overhead and democratize administrative
> expertise.
> * *Natural Language Diagnostics & Troubleshooting:* This is the most
> immediate and compelling use case. Administrators, regardless of their
> expertise level, can interact with the cluster in plain English to diagnose
> issues. Instead of navigating through multiple screens in the Ambari UI or
> crafting complex {{curl}} commands, they can simply ask questions. For
> instance:
> ** _"Why did the HDFS service health check fail on node '<nodeName>?"_
> ** _"Show me all CRITICAL alerts from the last 24 hours related to YARN."_
> ** _"What is the current heap usage of the NameNode, and how does it compare
> to yesterday?"_ To answer these, an AI agent would leverage MCP {{Resources}}
> to fetch health reports, alert histories, and performance metrics from
> Ambari, then use its reasoning capabilities to synthesize a coherent,
> human-readable answer.
> * *Automated and Agentic Remediation:* Moving beyond diagnosis, this
> integration empowers AI agents to take corrective actions. This creates a
> "self-healing" capability for the cluster. An agent can be instructed to
> execute complex remediation workflows that involve a chain of actions and
> checks. For example:
>
> ** _"The NameNode is in standby. Investigate the logs for critical errors.
> If none are found within the last 15 minutes, attempt a restart and confirm
> it becomes active. Notify the support channel in chat interface with the
> result."_ This workflow would require the agent to chain multiple MCP
> {{Tool}} calls: get logs ({{{}Resource{}}}), analyze them (LLM reasoning),
> restart the service ({{{}Tool{}}}), and check its status ({{{}Resource{}}}),
> demonstrating a sophisticated, agentic process.
> * *Conversational Configuration and Security Audits:* Complex configuration
> changes and security hardening are often error-prone. A conversational
> interface simplifies these tasks significantly.
>
> ** _"Increase the YARN NodeManager memory to 32GB on all worker nodes and
> then perform a rolling restart of the YARN service."_
> *
> ** _"Audit the cluster for security compliance. List all services that do
> not have Kerberos enabled and generate the sequence of API calls required to
> configure them."_ These commands would be translated by the agent into a
> series of {{updateServiceConfig}} and {{restartService}} tool calls, executed
> in the correct order.
> * *Declarative Provisioning via Conversation:* This use case represents an
> evolution of Ambari Blueprints, making cluster provisioning more accessible.
> An administrator could describe the desired cluster in high-level terms, and
> the AI agent would handle the low-level details of creating the Blueprint
> JSON.
>
> ** _"Provision a new 5-node test cluster using <stack name and version>. The
> cluster should include HDFS, YARN, and Spark. Designate 'master01' as the
> master node with the NameNode and ResourceManager, and the rest as worker
> nodes with DataNodes and NodeManagers."_ The agent would parse this request,
> generate the corresponding Blueprint, and use an MCP {{Tool}} to submit it to
> the Ambari API, initiating the cluster deployment.
> * *Proposed Solution:* This feature proposes the development and integration
> of a new, standalone {*}Ambari MCP Server{*}. This service will expose
> Ambari's rich management capabilities through the open and rapidly-adopted
> Model Context Protocol (MCP). By doing so, it will allow any MCP-compatible
> AI agent or host application (e.g., VS Code with Copilot, Claude Desktop) to
> securely discover and interact with the Ambari-managed cluster. The server
> will map Ambari's REST API endpoints to MCP's core primitives: state-changing
> operations will be exposed as {{{}Tools{}}}, read-only data queries as
> {{{}Resources{}}}, and complex, multi-step administrative tasks as
> {{{}Prompts{}}}. This will effectively transform Ambari from a passive
> management tool into an active, intelligent platform accessible via natural
> language and agentic workflows.
> *Key Benefits:*
> *Reduced Operational Overhead:* Enable administrators to diagnose issues,
> perform restarts, and modify configurations using simple, conversational
> commands, automating routine tasks.
> *Democratized Expertise:* Allow less experienced operators to perform complex
> administrative operations safely by leveraging pre-defined, reliable MCP
> Prompts that encapsulate expert workflows.
> *Enhanced Automation and Self-Healing:* Provide the foundation for building
> sophisticated, agentic systems that can proactively monitor cluster health,
> diagnose failures, and execute remediation plans autonomously.
> *Ecosystem Interoperability:* Position Ambari as a first-class citizen in the
> burgeoning ecosystem of AI development tools and agentic frameworks by
> adopting the MCP standard, ensuring its future relevance.
> *Roadmap:*
> *
> ** Read-Only Integration (The Observer) - Phase 1: Exposing all relevant
> cluster state, including service statuses, host information, component
> layouts, configurations, alert histories, and performance metrics.
> ** Actionable Tools (The Operator) - Phase 2: Enable direct, conversational
> control over the cluster. Administrators can now use the AI agent as a remote
> control for Ambari, issuing commands to operate the cluster.
> ** Abstracted Workflows (The Autonomous Agent) - Phase 3: Achieve true
> agentic behavior. This phase moves beyond simple command-and-control to a
> state where the AI can be delegated complex, long-running tasks, executing
> sophisticated strategies with minimal human intervention and unlocking the
> full potential of autonomous data platform management.
> Refer [https://modelcontextprotocol.io/]
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