# MCP Resources

The server provides the following resources that can be accessed through the MCP protocol:

## kafka-mcp\://overview

Comprehensive summary of Kafka cluster health including broker counts, controller status, topic/partition metrics, and replication health. Use this resource for quick cluster status assessment and monitoring dashboards.

**Example Response:**

```json
{
  "timestamp": "2023-08-15T12:34:56Z",
  "broker_count": 3,
  "controller_id": 1,
  "topic_count": 24,
  "partition_count": 120,
  "under_replicated_partitions": 0,
  "offline_partitions": 0,
  "offline_broker_ids": [],
  "health_status": "healthy"
}
```

## kafka-mcp\://health-check

Detailed health assessment of the Kafka cluster covering broker availability, controller status, partition health, and consumer group performance. Provides actionable insights for troubleshooting and maintenance.

**Example Response:**

```json
{
  "timestamp": "2023-08-15T12:34:56Z",
  "broker_status": {
    "total_brokers": 3,
    "offline_brokers": 0,
    "offline_broker_ids": [],
    "status": "healthy"
  },
  "controller_status": {
    "controller_id": 1,
    "status": "healthy"
  },
  "partition_status": {
    "total_partitions": 120,
    "under_replicated_partitions": 0,
    "offline_partitions": 0,
    "status": "healthy"
  },
  "consumer_status": {
    "total_groups": 5,
    "groups_with_high_lag": 0,
    "status": "healthy",
    "error": ""
  },
  "overall_status": "healthy"
}
```

## kafka-mcp\://under-replicated-partitions

Comprehensive analysis of partitions with insufficient replication, including affected topics, missing replicas, and troubleshooting recommendations. Critical for identifying and resolving data durability issues.

**Example Response:**

```json
{
  "timestamp": "2023-08-15T12:34:56Z",
  "under_replicated_partition_count": 2,
  "details": [
    {
      "topic": "orders",
      "partition": 3,
      "leader": 1,
      "replica_count": 3,
      "isr_count": 2,
      "replicas": [1, 2, 3],
      "isr": [1, 2],
      "missing_replicas": [3]
    },
    {
      "topic": "clickstream",
      "partition": 5,
      "leader": 2,
      "replica_count": 3,
      "isr_count": 2,
      "replicas": [2, 3, 1],
      "isr": [2, 1],
      "missing_replicas": [3]
    }
  ],
  "recommendations": [
    "Check broker health for any offline or struggling brokers",
    "Verify network connectivity between brokers",
    "Monitor disk space on broker nodes",
    "Review broker logs for detailed error messages",
    "Consider increasing replication timeouts if network is slow"
  ]
}
```

## kafka-mcp\://consumer-lag-report

Detailed analysis of consumer group performance including lag metrics, group states, partition assignments, and performance recommendations. Supports threshold-based alerting and performance optimization. Accepts an optional "threshold" query parameter to set the lag threshold.

**Example Response:**

```json
{
  "timestamp": "2023-08-15T12:34:56Z",
  "lag_threshold": 1000,
  "group_count": 3,
  "group_summary": [
    {
      "group_id": "order-processor",
      "state": "Stable",
      "member_count": 2,
      "topic_count": 1,
      "total_lag": 15420,
      "has_high_lag": true
    },
    {
      "group_id": "analytics-pipeline",
      "state": "Stable",
      "member_count": 3,
      "topic_count": 2,
      "total_lag": 520,
      "has_high_lag": false
    }
  ],
  "high_lag_details": [
    {
      "group_id": "order-processor",
      "topic": "orders",
      "partition": 2,
      "current_offset": 1045822,
      "log_end_offset": 1061242,
      "lag": 15420
    }
  ],
  "recommendations": [
    "Check consumer instances for errors or slowdowns",
    "Consider scaling up consumer groups with high lag",
    "Review consumer configuration settings",
    "Examine processing bottlenecks in consumer application logic"
  ]
}
```


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