Integration

Confluent Kafka integration

Confluent Cloud REST API for managing Apache Kafka clusters, topics, connectors, and schema registry. Produce and consume messages, manage ACLs, and monitor Kafka infrastructure.

Connect Confluent Kafka How integrations work

Ask it in chat

Say what should happen.
The workflow builds itself.

When this happens, do that in Confluent Kafka.Built from the 26 Confluent Kafka tools below, on the connection you add once.

Common Confluent Kafka use cases

automate connector management
automate topic management
automate schema management
automate cluster management
automate subject management
automate topic config management
automate consumer group management
get topic programmatically
get schema programmatically
list topic programmatically

What you can do with Confluent Kafka

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Connector, 6 operations

create_connectorCreate connectorPOSTCreate a new connector in a Confluent Cloud Connect cluster. Provide the connector name, class, and configuration properties
delete_connectorDelete connectorDELETEDelete a connector from a Confluent Cloud Connect cluster. This stops the connector and removes its configuration
get_connectorGet connectorGETGet information about a specific connector including its configuration, status, and task details
list_connectorsList connectorsGETList all connectors in a Confluent Cloud Connect cluster. Returns connector names, types, and current status
pause_connectorPause connectorPUTPause a running connector in a Confluent Cloud Connect cluster. The connector and its tasks stop processing data until resumed
resume_connectorResume connectorPUTResume a paused connector in a Confluent Cloud Connect cluster. The connector and its tasks resume processing data from where they left off

Topic, 4 operations

create_topicCreate topicPOSTCreate a new topic in a Kafka cluster. Specify the topic name, partition count, replication factor, and optional configuration overrides
delete_topicDelete topicDELETEDelete a topic from a Kafka cluster. This permanently removes the topic and all its data
get_topicGet topicGETGet detailed information about a specific topic including its partition count, replication factor, and configuration
list_topicsList topicsGETList all topics in a Kafka cluster. Returns topic names, partition counts, replication factors, and whether the topic is internal

Schema, 3 operations

get_schema_by_idGet schema by idGETRetrieve a specific schema by its globally unique ID. Returns the schema definition, type, and references
list_schemasList schemasGETList all schemas registered in the Schema Registry. Returns schema IDs, types (AVRO, JSON, PROTOBUF), and subject associations
register_schemaRegister schemaPOSTRegister a new schema version under a subject in the Schema Registry. If the schema is compatible with the existing versions, it is assigned a new version number and globally unique ID

Cluster, 2 operations

get_clusterGet clusterGETGet detailed information about a specific Kafka cluster including its configuration, endpoint URLs, and metadata
list_clustersList clustersGETList all Kafka clusters accessible to the authenticated user. Returns cluster IDs, names, availability zones, and endpoint URLs needed for all other cluster-scoped operations

Consumer group, 2 operations

get_consumer_groupGet consumer groupGETGet detailed information about a specific consumer group including its state, members, partition assignments, and lag
list_consumer_groupsList consumer groupsGETList all consumer groups in a Kafka cluster. Returns group IDs, states, coordinator information, and partition assignor

Subject, 2 operations

delete_subjectDelete subjectDELETEDelete a subject and all its associated schema versions from the Schema Registry. By default performs a soft delete; use permanent=true for hard delete
list_subjectsList subjectsGETList all subjects registered in the Schema Registry. Subjects typically follow the naming convention <topic>-key or <topic>-value

Topic config, 2 operations

list_topic_configsList topic configsGETList all configuration parameters for a specific topic. Returns current values, defaults, and whether each config is read-only or dynamic
update_topic_configUpdate topic configPUTUpdate a single configuration parameter for a topic. Use this to change settings like retention.ms, cleanup.policy, max.message.bytes, etc.

Connector config, 1 operation

update_connector_configUpdate connector configPUTUpdate the configuration of an existing connector in a Confluent Cloud Connect cluster. Replaces the entire connector configuration with the provided config object

Environment, 1 operation

list_environmentsList environmentsGETList all environments accessible to the authenticated user. Returns environment IDs, names, and metadata needed for connector and cluster management operations

Partition, 1 operation

list_partitionsList partitionsGETList all partitions for a specific topic. Returns partition IDs, leaders, replicas, and in-sync replica information

Produce record, 1 operation

produce_recordsProduce recordsPOSTProduce one or more records to a Kafka topic. Supports specifying partition, key, value, headers, and timestamp. Values can be JSON, string, or binary (base64-encoded)

Subject version, 1 operation

get_subject_versionsGet subject versionsGETList all schema versions registered under a specific subject. Returns version numbers that can be used to retrieve individual schema versions

Use Confluent Kafka from Claude Code, Cursor or Codex

Connect Confluent Kafka in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Confluent Kafka operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Confluent Kafka

Create your workspace
Sign up free. Nothing to install.
Connect Confluent Kafka once
Add your Confluent Kafka connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Confluent Kafka integration FAQ

What can you do with the Confluent Kafka integration?

LiveContext exposes 26 operations on Confluent Kafka, covering connector, topic, schema and cluster. Common uses include automate connector management, automate topic management and automate schema management.