> For the complete documentation index, see [llms.txt](https://docs.tuannvm.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tuannvm.com/kafka-mcp-server/docs/resources.md).

# 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"
  ]
}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.tuannvm.com/kafka-mcp-server/docs/resources.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.