mcp-ambari-api 3.6.3
Model Context Protocol (MCP) server for Apache Ambari API integration. Provides comprehensive tools for managing Hadoop clusters including service operations, configuration management, status monitoring, and request tracking.
Requires Python: >=3.12

# MCP Ambari API - Apache Hadoop Cluster Management Automation

> **🚀 Automate Apache Ambari operations with AI/LLM**: Conversational control for Hadoop cluster management, service monitoring, configuration inspection, and precise Ambari Metrics queries via Model Context Protocol (MCP) tools.

---

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---

## Architecture & Internal (DeepWiki)

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---

## 📋 Overview

**MCP Ambari API** is a powerful Model Context Protocol (MCP) server that enables seamless Apache Ambari cluster management through natural language commands. Built for DevOps engineers, data engineers, and system administrators who work with Hadoop ecosystems.

### Features

- ✅ **Interactive Ambari Operations Hub** – Provides an MCP-based foundation for querying and managing services through natural language instead of console or UI interfaces.
- ✅ **Real-time Cluster Visibility** – Comprehensive view of key metrics including service status, host details, alert history, and ongoing requests in a single interface.
- ✅ **Metrics Intelligence Pipeline** – Dynamically discovers and filters AMS appIds and metric names, connecting directly to time-series analysis workflows.
- ✅ **Automated Operations Workflow** – Consolidates repetitive start/stop operations, configuration checks, user queries, and request tracking into consistent scenarios.
- ✅ **Built-in Operational Reports** – Instantly delivers dfsadmin-style HDFS reports, service summaries, and capacity metrics through LLM or CLI interfaces.
- ✅ **Safety Guards and Guardrails** – Requires user confirmation before large-scale operations and provides clear guidance for risky commands through prompt templates.
- ✅ **LLM Integration Optimization** – Includes natural language examples, parameter mapping, and usage guides to ensure stable AI agent operations.
- ✅ **Flexible Deployment Models** – Supports stdio/streamable-http transport, Docker Compose, and token authentication for deployment across development and production environments.
- ✅ **Performance-Oriented Caching Architecture** – Built-in AMS metadata cache and request logging ensure fast responses even in large-scale clusters.
- ✅ **Scalable Code Architecture** – Asynchronous HTTP, structured logging, and modularized tool layers enable easy addition of new features.
- ✅ **Production-Validated** – Based on tools validated in test Ambari clusters, ready for immediate use in production environments.
- ✅ **Diversified Deployment Channels** – Available through PyPI packages, Docker images, and other preferred deployment methods.

### Docuement for Airflow REST-API

- [Ambari API Documents](https://github.com/apache/ambari/blob/trunk/ambari-server/docs/api/v1/index.md)

## Topics

`apache-ambari` `hadoop-cluster` `mcp-server` `cluster-automation` `devops-tools` `big-data` `infrastructure-management` `ai-automation` `llm-tools` `python-mcp`

---

## Example Queries - Cluster Info/Status

### [Go to More Example Queries](./src/mcp_ambari_api/prompt_template.md#9-example-queries)

---

![Example: Querying Ambari Cluster(1)](img/ex-screenshot-1.png)

---

![Example: Querying Ambari Cluster(2)](img/ex-screenshot-hdfs-dfsadmin-report.png)

---

## 🚀 QuickStart Guide /w Docker

> **Note:** The following instructions assume you are using the `streamable-http` mode for MCP Server.

### Flow Diagram of Quickstart/Tutorial

![Flow Diagram of Quickstart/Tutorial](img/MCP-Workflow-of-Quickstart-Tutorial.png)

### 1. Prepare Ambari Cluster (Test Target)

To set up a Ambari Demo cluster, follow the guide at: [Install Ambari 3.0 with Docker](https://medium.com/@call518/install-ambari-3-0-with-docker-297a8bb108c8)

![Example: Ambari Demo Cluster](img/ex-ambari.png)

### 2. Run Docker-Compose

Start the `MCP-Server`, `MCPO`(MCP-Proxy for OpenAPI), and `OpenWebUI`.

1. Ensure Docker and Docker Compose are installed on your system.
1. Clone this repository and navigate to its root directory.
1. **Set up environment configuration:**
   ```bash
   # Copy environment template and configure your settings
   cp .env.example .env
   # Edit .env with your Ambari cluster information
   ```
1. **Configure your Ambari connection in `.env` file:**
   ```bash
   # Ambari cluster connection
   AMBARI_HOST=host.docker.internal
   AMBARI_PORT=7070
   AMBARI_USER=admin
   AMBARI_PASS=admin
   AMBARI_CLUSTER_NAME=TEST-AMBARI

   # Ambari Metrics (AMS) collector
   AMBARI_METRICS_HOST=host.docker.internal
   AMBARI_METRICS_PORT=16188
   AMBARI_METRICS_PROTOCOL=http
   AMBARI_METRICS_TIMEOUT=15
   
   # (Optional) Enable authentication for streamable-http mode
   # Recommended for production environments
   REMOTE_AUTH_ENABLE=false
   REMOTE_SECRET_KEY=your-secure-secret-key-here
   ```
1. Run:
   ```bash
   docker-compose up -d
   ```

- OpenWebUI will be available at: `http://localhost:${DOCKER_EXTERNAL_PORT_OPENWEBUI}` (default: 3001)
- The MCPO-Proxy will be accessible at: `http://localhost:${DOCKER_EXTERNAL_PORT_MCPO_PROXY}` (default: 8001)  
- The MCPO API Docs: `http://localhost:${DOCKER_EXTERNAL_PORT_MCPO_PROXY}/mcp-ambari-api/docs`

![Example: MCPO-Proxy](img/mcpo-proxy-api-docs.png)

### 3. Registering the Tool in OpenWebUI

> 📌 **Note**: Web-UI configuration instructions are based on OpenWebUI **v0.6.22**. Menu locations and settings may differ in newer versions.

1. logging in to OpenWebUI with an admin account
1. go to "Settings" → "Tools" from the top menu.
1. Enter the `mcp-ambari-api` Tool address (e.g., `http://localhost:8000/mcp-ambari-api`) to connect MCP Tools with your Ambari cluster.

### 4. More Examples: Using MCP Tools to Query Ambari Cluster

Below is an example screenshot showing how to query the Ambari cluster using MCP Tools in OpenWebUI:

#### Example Query - Cluster Configuration Review & Recommendations

![Example: Querying Ambari Cluster(2)](img/ex-screenshot-2.png)

#### Example Query - Restart HDFS Service

![Example: Querying Ambari Cluster(3)](img/ex-screenshot-3-1.png)
![Example: Querying Ambari Cluster(3)](img/ex-screenshot-3-2.png)

---

## 📈 Metrics & Trends

- **Terminology quick reference**
  - **appId**: Ambari Metrics Service groups every metric under an application identifier (e.g., `namenode`, `datanode`, `ambari_server`, `HOST`). Think of it as the component or service emitting that timeseries.
  - **metric name**: The fully qualified string Ambari uses for each timeseries (e.g., `jvm.JvmMetrics.MemHeapUsedM`, `dfs.datanode.BytesWritten`). Exact names are required when querying AMS.

- `list_common_metrics_catalog`: keyword search against the live metadata-backed metric catalog (cached locally). Use `search="heap"` or similar to narrow suggestions before running a time-series query.  
  _Example_: “Show the heap-related metrics available for the NameNode appId.”
- `list_ambari_metric_apps`: list discovered AMS `appId` values, optionally including metric counts; pass `refresh=true` or `limit` to control output.  
  _Example_: “List every appId currently exposed by AMS.”
- The natural-language query “AMS에서 사용 가능한 appId 목록만 보여줘” maps to `list_ambari_metric_apps` and returns the exact identifiers you can copy into other tools.
- `list_ambari_metrics_metadata`: raw AMS metadata explorer (supports `app_id`, `metric_name_filter`, `host_filter`, `search`, adjustable `limit`, default 50).  
  _Example_: “Give me CPU-related metric metadata under HOST.”
- `query_ambari_metrics`: fetch time-series data; the tool auto-selects curated metric names, falls back to metadata search when needed, and honors Ambari's default precision unless you explicitly supply `precision="SECONDS"`, etc.  
  _Examples_: “Plot the last 30 minutes of `jvm.JvmMetrics.MemHeapUsedM` for the NameNode.” / “Compare `jvm.JvmMetrics.MemHeapUsedM` for DataNode hosts `bigtop-hostname0.demo.local` and `bigtop-hostname1.demo.local` over the past 30 minutes.”
- `hdfs_dfadmin_report`: produce a DFSAdmin-style capacity/DataNode summary (mirrors `hdfs dfsadmin -report`).

**Live Metric Catalog (via AMS metadata)**
- Metric names are discovered on demand from `/ws/v1/timeline/metrics/metadata` and cached for quick reuse.
- Use `list_common_metrics_catalog` or the `ambari-metrics://catalog/all` resource (append `?refresh=true` to bypass the cache) to inspect the latest `appId → metric` mapping. Query `ambari-metrics://catalog/apps` to list appIds or `ambari-metrics://catalog/<appId>` for a single app.
- Typical appIds include `ambari_server`, `namenode`, `datanode`, `nodemanager`, `resourcemanager`, and `HOST`, but the list adapts to whatever the Ambari Metrics service advertises in your cluster.

---

## 🔍 Ambari Metrics Query Requirements (Exact-Match Workflow)

Recent updates removed natural-language metric guessing in favor of deterministic, catalog-driven lookups. Keep the following rules in mind when you (or an LLM agent) call `query_ambari_metrics`:

1. **Always pass an explicit `app_id`.** If it is missing or unsupported, the tool returns a list of valid appIds and aborts so you can choose one manually.
2. **Specify exact metric names.** Use `list_common_metrics_catalog(app_id="<target>", search="keyword")`, `list_ambari_metric_apps` (to discover appIds), or the `ambari-metrics://catalog/<appId>` resource to browse the live per-app metric set and copy the identifier (e.g., `jvm.JvmMetrics.MemHeapUsedM`).
3. **Host-scope behavior**: When `hostnames` is omitted the API returns cluster-wide aggregates. Provide one or more hosts (comma-separated) to focus on specific nodes.
4. **No fuzzy matches.** The server now calls Ambari exactly as requested. If the metric is wrong or empty, Ambari will simply return no datapoints—double-check the identifier via `/ws/v1/timeline/metrics/metadata`.

Example invocation:

```plaintext
query_ambari_metrics(
  metric_names="jvm.JvmMetrics.MemHeapUsedM",
  app_id="nodemanager",
  duration="1h",
  group_by_host=true
)
```

For multi-metric lookups, pass a comma-separated list of exact names. Responses document any auto-applied host filters so you can copy/paste them into subsequent requests.

---

## 🐛 Usage & Configuration

This MCP server supports two connection modes: **stdio** (traditional) and **streamable-http** (Docker-based). You can configure the transport mode using CLI arguments or environment variables.

**Configuration Priority:** CLI arguments > Environment variables > Default values

### CLI Arguments

- `--type` (`-t`): Transport type (`stdio` or `streamable-http`) - Default: `stdio`
- `--host`: Host address for HTTP transport - Default: `127.0.0.1`  
- `--port` (`-p`): Port number for HTTP transport - Default: `8000`
- `--auth-enable`: Enable Bearer token authentication for streamable-http mode - Default: `false`
- `--secret-key`: Secret key for Bearer token authentication (required when auth enabled)

### Environment Variables

| Variable | Description | Default | Project Default |
|----------|-------------|---------|-----------------|
| `PYTHONPATH` | Python module search path for MCP server imports | - | `/app/src` |
| `MCP_LOG_LEVEL` | Server logging verbosity (DEBUG, INFO, WARNING, ERROR) | `INFO` | `INFO` |
| `FASTMCP_TYPE` | MCP transport protocol (stdio for CLI, streamable-http for web) | `stdio` | `streamable-http` |
| `FASTMCP_HOST` | HTTP server bind address (0.0.0.0 for all interfaces) | `127.0.0.1` | `0.0.0.0` |
| `FASTMCP_PORT` | HTTP server port for MCP communication | `8000` | `8000` |
| `REMOTE_AUTH_ENABLE` | Enable Bearer token authentication for streamable-http mode<br/>**Default: false** (if undefined, empty, or null) | `false` | `false` |
| `REMOTE_SECRET_KEY` | Secret key for Bearer token authentication<br/>**Required when REMOTE_AUTH_ENABLE=true** | - | `your-secret-key-here` |
| `AMBARI_HOST` | Ambari server hostname or IP address | `127.0.0.1` | `host.docker.internal` |
| `AMBARI_PORT` | Ambari server port number | `8080` | `8080` |
| `AMBARI_USER` | Username for Ambari server authentication | `admin` | `admin` |
| `AMBARI_PASS` | Password for Ambari server authentication | `admin` | `admin` |
| `AMBARI_CLUSTER_NAME` | Name of the target Ambari cluster | `TEST-AMBARI` | `TEST-AMBARI` |
| `DOCKER_EXTERNAL_PORT_OPENWEBUI` | Host port mapping for Open WebUI container | `8080` | `3001` |
| `DOCKER_EXTERNAL_PORT_MCP_SERVER` | Host port mapping for MCP server container | `8080` | `18001` |
| `DOCKER_EXTERNAL_PORT_MCPO_PROXY` | Host port mapping for MCPO proxy container | `8000` | `8001` |

**Note**: `AMBARI_CLUSTER_NAME` serves as the default target cluster for operations when no specific cluster is specified. All environment variables can be configured via the `.env` file. 

**Transport Selection Logic:**

**Configuration Priority:** CLI arguments > Environment variables > Default values

**Transport Selection Logic:**

- **CLI Priority**: `--type streamable-http --host 0.0.0.0 --port 18001`
- **Environment Priority**: `FASTMCP_TYPE=streamable-http FASTMCP_HOST=0.0.0.0 FASTMCP_PORT=18001`
- **Legacy Support**: `FASTMCP_PORT=18001` (automatically enables streamable-http mode)
- **Default**: `stdio` mode when no configuration is provided

### Environment Setup

```bash
# 1. Clone the repository
git clone https://github.com/call518/MCP-Ambari-API.git
cd MCP-Ambari-API

# 2. Set up environment configuration
cp .env.example .env

# 3. Configure your Ambari connection in .env file
AMBARI_HOST=your-ambari-host
AMBARI_PORT=your-ambari-port  
AMBARI_USER=your-username
AMBARI_PASS=your-password
AMBARI_CLUSTER_NAME=your-cluster-name
```

---

## 🔐 Security & Authentication

### Bearer Token Authentication

For `streamable-http` mode, this MCP server supports Bearer token authentication to secure remote access. This is especially important when running the server in production environments.

#### Configuration

**Enable Authentication:**

```bash
# In .env file
REMOTE_AUTH_ENABLE=true
REMOTE_SECRET_KEY=your-secure-secret-key-here
```

**Or via CLI:**

```bash
python -m mcp_ambari_api --type streamable-http --auth-enable --secret-key your-secure-secret-key-here
```

#### Security Levels

1. **stdio mode** (Default): Local-only access, no authentication needed
2. **streamable-http + REMOTE_AUTH_ENABLE=false/undefined**: Remote access without authentication ⚠️ **NOT RECOMMENDED for production**
3. **streamable-http + REMOTE_AUTH_ENABLE=true**: Remote access with Bearer token authentication ✅ **RECOMMENDED for production**

> **🔒 Default Policy**: `REMOTE_AUTH_ENABLE` defaults to `false` if undefined, empty, or null. This ensures the server starts even without explicit authentication configuration.

#### Client Configuration

When authentication is enabled, MCP clients must include the Bearer token in the Authorization header:

```json
{
  "mcpServers": {
    "mcp-ambari-api": {
      "type": "streamable-http",
      "url": "http://your-server:8000/mcp",
      "headers": {
        "Authorization": "Bearer your-secure-secret-key-here"
      }
    }
  }
}
```

#### Security Best Practices

- **Always enable authentication** when using streamable-http mode in production
- **Use strong, randomly generated secret keys** (32+ characters recommended)
- **Use HTTPS** when possible (configure reverse proxy with SSL/TLS)
- **Restrict network access** using firewalls or network policies
- **Rotate secret keys regularly** for enhanced security
- **Monitor access logs** for unauthorized access attempts

#### Error Handling

When authentication fails, the server returns:
- **401 Unauthorized** for missing or invalid tokens
- **Detailed error messages** in JSON format for debugging

---

### Method 1: Local MCP (transport="stdio")

```json
{
  "mcpServers": {
    "mcp-ambari-api": {
      "command": "uvx",
      "args": ["--python", "3.12", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "host.docker.internal",
        "AMBARI_PORT": "8080",
        "AMBARI_USER": "admin",
        "AMBARI_PASS": "admin",
        "AMBARI_CLUSTER_NAME": "TEST-AMBARI",
        "MCP_LOG_LEVEL": "INFO"
      }
    }
  }
}
```

### Method 2: Remote MCP (transport="streamable-http")

**On MCP-Client Host:**

```json
{
  "mcpServers": {
    "mcp-ambari-api": {
      "type": "streamable-http",
      "url": "http://localhost:18001/mcp"
    }
  }
}
```

**With Bearer Token Authentication (Recommended for production):**

```json
{
  "mcpServers": {
    "mcp-ambari-api": {
      "type": "streamable-http", 
      "url": "http://localhost:18001/mcp",
      "headers": {
        "Authorization": "Bearer your-secure-secret-key-here"
      }
    }
  }
}
```

---

## Example usage: Claude-Desktop

**claude_desktop_config.json**

```json
{
  "mcpServers": {
    "mcp-ambari-api": {
      "command": "uvx",
      "args": ["--python", "3.12", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "localhost",
        "AMBARI_PORT": "7070",
        "AMBARI_USER": "admin",
        "AMBARI_PASS": "admin",
        "AMBARI_CLUSTER_NAME": "TEST-AMBARI",
        "MCP_LOG_LEVEL": "INFO"
      }
    }
  }
}
```

![Example: Claude-Desktop(3)](img/ex-screenshot-claude-desktop-001.png)

**(Option) Configure Multiple Ambari Cluster**

```json
{
  "mcpServers": {
    "Ambari-Cluster-A": {
      "command": "uvx",
      "args": ["--python", "3.12", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "a.foo.com",
        "AMBARI_PORT": "8080",
        "AMBARI_USER": "admin-user",
        "AMBARI_PASS": "admin-pass",
        "AMBARI_CLUSTER_NAME": "AMBARI-A",
        "MCP_LOG_LEVEL": "INFO"
      }
    },
    "Ambari-Cluster-B": {
      "command": "uvx",
      "args": ["--python", "3.12", "mcp-ambari-api"],
      "env": {
        "AMBARI_HOST": "b.bar.com",
        "AMBARI_PORT": "8080",
        "AMBARI_USER": "admin-user",
        "AMBARI_PASS": "admin-pass",
        "AMBARI_CLUSTER_NAME": "AMBARI-B",
        "MCP_LOG_LEVEL": "INFO"
      }
    }
  }
}
```

**Remote Access with Authentication (Claude Desktop):**

```json
{
  "mcpServers": {
    "mcp-ambari-api-remote": {
      "type": "streamable-http",
      "url": "http://your-server-ip:18001/mcp",
      "headers": {
        "Authorization": "Bearer your-secure-secret-key-here"
      }
    }
  }
}
```

---

## 🎯 Core Features & Capabilities

### Service Operations

- **Hadoop Service Management**: Start, stop, restart HDFS, YARN, Spark, HBase, and more
- **Bulk Operations**: Control all cluster services simultaneously
- **Status Monitoring**: Real-time service health and performance tracking

### Configuration Management

- **Unified Config Tool**: Single interface for all configuration types (yarn-site, hdfs-site, etc.)
- **Bulk Configuration**: Export and manage multiple configurations with filtering
- **Configuration Validation**: Syntax checking and validation before applying changes

### Monitoring & Alerting

- **Real-time Alerts**: Current and historical cluster alerts with filtering
- **Request Tracking**: Monitor long-running operations with detailed progress
- **Host Monitoring**: Hardware metrics, component states, and resource utilization

### Administration

- **User Management**: Check cluster user administration
- **Host Management**: Node registration, component assignments, and health monitoring

---

## Available MCP Tools

This MCP server provides the following tools for Ambari cluster management:

### Cluster Management

- `get_cluster_info` - Retrieve basic cluster information and status
- `get_active_requests` - List currently active/running operations
- `get_request_status` - Check status and progress of specific requests

### Service Management

- `get_cluster_services` - List all services with their status
- `get_service_status` - Get detailed status of a specific service
- `get_service_components` - List components and host assignments for a service
- `get_service_details` - Get comprehensive service information
- `start_service` - Start a specific service
- `stop_service` - Stop a specific service
- `restart_service` - Restart a specific service
- `start_all_services` - Start all services in the cluster
- `stop_all_services` - Stop all services in the cluster
- `restart_all_services` - Restart all services in the cluster

### Configuration Tools

- `dump_configurations` - Unified configuration tool (replaces `get_configurations`, `list_configurations`, and the former internal `dump_all_configurations`). Supports:
  - Single type: `dump_configurations(config_type="yarn-site")`
  - Bulk summary: `dump_configurations(summarize=True)`
  - Filter by substring (type or key): `dump_configurations(filter="memory")`
  - Service filter (narrow types by substring): `dump_configurations(service_filter="yarn", summarize=True)`
  - Keys only (no values): `dump_configurations(include_values=False)`
  - Limit number of types: `dump_configurations(limit=10, summarize=True)`

> Breaking Change: `get_configurations` and `list_configurations` were removed in favor of this single, more capable tool.

### Host Management

- `list_hosts` - List all hosts in the cluster
- `get_host_details` - Get detailed information for specific or all hosts (includes component states, hardware metrics, and service assignments)

### User Management

- `list_users` - List all users in the Ambari system with their usernames and API links
- `get_user` - Get detailed information about a specific user including:
  - Basic profile (ID, username, display name, user type)
  - Status information (admin privileges, active status, login failures)
  - Authentication details (LDAP user status, authentication sources)
  - Group memberships, privileges, and widget layouts

### Alert Management

- `get_alerts_history` - **Unified alert tool** for both current and historical alerts:
  - **Current mode** (`mode="current"`): Retrieve current/active alerts with real-time status
    - Current alert states across cluster, services, or hosts
    - Maintenance mode filtering (ON/OFF)
    - Summary formats: basic summary and grouped by definition
    - Detailed alert information including timestamps and descriptions
  - **History mode** (`mode="history"`): Retrieve historical alert events from the cluster
    - Scope filtering: cluster-wide, service-specific, or host-specific alerts
    - Time range filtering: from/to timestamp support
    - Pagination support for large datasets
  - **Common features** (both modes):
    - State filtering: CRITICAL, WARNING, OK, UNKNOWN alerts
    - Definition filtering: filter by specific alert definition names
    - Multiple output formats: detailed, summary, compact
    - Unified API for consistent alert querying experience

---

## 🤝 Contributing & Support

### How to Contribute

- 🐛 **Report Bugs**: [GitHub Issues](https://github.com/call518/MCP-Ambari-API/issues)
- 💡 **Request Features**: [Feature Requests](https://github.com/call518/MCP-Ambari-API/issues)  
- 🔧 **Submit PRs**: [Contributing Guidelines](https://github.com/call518/MCP-Ambari-API/blob/main/CONTRIBUTING.md)
- 📖 **Improve Docs**: Help make documentation better

### Technologies Used

- **Language**: Python 3.12
- **Framework**: Model Context Protocol (MCP)
- **API**: Apache Ambari REST API
- **Transport**: stdio (local) and streamable-http (remote)
- **Deployment**: Docker, Docker Compose, PyPI

### Dev Env.

- WSL2(networkingMode = bridged) + Docker-Desktop
  - `.wslconfig`: tested with `networkingMode = bridged`
- Python 3.12 venv

  ```bash
  ### Option-1: with uv
  uv venv --python 3.12 --seed

  ### Option-2: with pip
  python3.12 -m venv .venv
  source .venv/bin/activate
  pip install -U pip
  ```

---

## 🛠️ Adding Custom Tools

After you've thoroughly explored the existing functionality, you might want to add your own custom tools for specific monitoring or management needs. This MCP server is designed for easy extensibility.

### Step-by-Step Guide

#### 1. **Add Helper Functions (Optional)**

Add reusable data functions to `src/mcp_ambari_api/functions.py`:

```python
async def get_your_custom_data(target_resource: str = None) -> List[Dict[str, Any]]:
    """Your custom data retrieval function."""
    # Example implementation - adapt to your Ambari service
    endpoint = f"/clusters/{AMBARI_CLUSTER_NAME}/your_custom_endpoint"
    if target_resource:
        endpoint += f"/{target_resource}"
    
    response_data = await make_ambari_request(endpoint)
    
    if response_data is None or "items" not in response_data:
        return []
    
    return response_data["items"]
```

#### 2. **Create Your MCP Tool**

Add your tool function to `src/mcp_ambari_api/mcp_main.py`:

```python
@mcp.tool()
@log_tool
async def get_your_custom_analysis(limit: int = 50, target_name: Optional[str] = None) -> str:
    """
    [Tool Purpose]: Brief description of what your tool does
    
    [Core Functions]:
    - Feature 1: Data aggregation and analysis
    - Feature 2: Resource monitoring and insights
    - Feature 3: Performance metrics and reporting
    
    [Required Usage Scenarios]:
    - When user asks "your specific analysis request"
    - Your business-specific monitoring needs
    
    Args:
        limit: Maximum results (1-100)
        target_name: Target resource/service name (optional)
    
    Returns:
        Formatted analysis results (success: formatted data, failure: English error message)
    """
    try:
        limit = max(1, min(limit, 100))  # Always validate input
        
        results = await get_your_custom_data(target_resource=target_name)
        
        if not results:
            return f"No custom analysis data found{' for ' + target_name if target_name else ''}."
        
        # Apply limit
        limited_results = results[:limit]
        
        # Format output
        result_lines = [
            f"Custom Analysis Results{' for ' + target_name if target_name else ''}",
            "=" * 50,
            f"Found: {len(limited_results)} items (total: {len(results)})",
            ""
        ]
        
        for i, item in enumerate(limited_results, 1):
            # Customize this formatting based on your data structure
            name = item.get("name", "Unknown")
            status = item.get("status", "N/A")
            result_lines.append(f"[{i}] {name}: {status}")
        
        return "\n".join(result_lines)
        
    except Exception as e:
        return f"Error: Exception occurred while retrieving custom analysis - {str(e)}"
```

#### 3. **Update Imports**

Add your helper function to the imports section in `src/mcp_ambari_api/mcp_main.py`:

```python
from mcp_ambari_api.functions import (
    format_timestamp,
    format_single_host_details,
    make_ambari_request,
    # ... existing imports ...
    get_your_custom_data,  # Add your new function here
)
```

#### 4. **Update Prompt Template (Recommended)**

Add your tool description to `src/mcp_ambari_api/prompt_template.md` for better AI recognition:

```markdown
### Custom Analysis Tools

**get_your_custom_analysis**
- "Show me custom analysis results"
- "Get custom analysis for target_name"
- "Display custom monitoring data"
- 📋 **Features**: Custom data aggregation, resource monitoring, performance insights
```

#### 5. **Test Your Tool**

```bash
# Local testing with MCP Inspector
./run-mcp-inspector-local.sh

# Or test with Docker environment
docker-compose up -d
docker-compose logs -f mcp-server

# Test with natural language queries:
# "Show me custom analysis results"
# "Get custom analysis for my_target"
```

### Important Notes

- **Always use `@mcp.tool()` and `@log_tool` decorators** for proper registration and logging
- **Follow the existing error handling patterns** - return English error messages starting with "Error:"
- **Use `make_ambari_request()` function** for all Ambari API calls to ensure consistent authentication and error handling
- **Validate all input parameters** before using them in API calls
- **Test thoroughly** with both valid and invalid inputs

### Example Use Cases

- **Custom service health checks** beyond standard Ambari monitoring
- **Specialized configuration validation** for your organization's standards  
- **Custom alert aggregation** and reporting formats
- **Integration with external monitoring systems** via Ambari data
- **Automated compliance checking** for cluster configurations

---

## ❓ Frequently Asked Questions

### Q: What Ambari versions are supported?

**A**: Ambari 2.7+ is recommended. Earlier versions may work but are not officially tested.

### Q: Can I use this with cloud-managed Hadoop clusters?

**A**: Yes, as long as Ambari API endpoints are accessible, it works with on-premise, cloud, and hybrid deployments.

### Q: How do I troubleshoot connection issues?

**A**: Check your `AMBARI_HOST`, `AMBARI_PORT`, and network connectivity. Enable debug logging with `MCP_LOG_LEVEL=DEBUG`.

### Q: How does this compare to Ambari Web UI?

**A**: This provides programmatic access via AI/LLM commands, perfect for automation, scripting, and integration with modern DevOps workflows.

---

## Contributing

🤝 **Got ideas? Found bugs? Want to add cool features?**

We're always excited to welcome new contributors! Whether you're fixing a typo, adding a new monitoring tool, or improving documentation - every contribution makes this project better.

**Ways to contribute:**
- 🐛 Report issues or bugs
- 💡 Suggest new Ambari monitoring features
- 📝 Improve documentation 
- 🚀 Submit pull requests
- ⭐ Star the repo if you find it useful!

**Pro tip:** The codebase is designed to be super friendly for adding new tools. Check out the existing `@mcp.tool()` functions in `mcp_main.py` and follow the [Adding Custom Tools](#️-adding-custom-tools) guide above.

---

## 📄 License

This project is licensed under the MIT License.