LLM reasoning with Axual platform workflows
Reads environments, topics, and deployments
Uses MCP tools to execute actions
Respects ownership, groups, and access policies
Works through Self-Service APIs
Axual MCP Server
Manage Kafka using natural language with AI
noneCreate a Kafka topic orders with 30 days retention in dev environment.Successfully created Kafka topic orders in dev environment.
The topic is now ready to use!Who is it for
Axual MCP server
For Developers
Build Faster on Axual.
- Discover topics and environments
- Create topics with correct ownership
- Generate realistic test data using KSML
- Deploy streaming applications with KSML or Flink SQL
- Debug by browsing real messages
For platform engineers
Control everything through Axual.
- Enforce environments (dev, staging, production)
- Manage ownership and groups
- Standardize Kafka workflows
- Enable safe self-service without exposing raw Kafka
For engineering leaders
Build Faster on Axual.
- Reduce onboarding time
- Increase developer productivity
- Maintain governance across teams
How Axual MCP server works
MCP Capabilities
- Topics: create, search, manage
- Deployments: inspect across environments
- Data: produce and browse messages
- Applications: create, search, manage
- KSML: generate KSML apps with AI and register/run them on Axual
- Access: request and enforce Kafka permissions
Kafka MCP use cases
What can you use Axual MCP server for:
Discover and understand Axual resources
- What topics does my team own?
- What environments are available?
- Where is this topic deployed?
Build and test
- Create topics with ownership and environment
- Produce realistic test data
- Register and deploy KSML applications
- Write and deploy Flink SQL jobs from a plain-language description.
Troubleshoot
- Why is no consumer reading from this topic?
- Show messages with errors in the last hour
Govern and optimize
- What topics are unused or misconfigured?
- Which topics require access changes?
- What should I fix before production?
Outcomes
Faster Axual Workflows
Reduced manual UI/API usage
Safer self-service within governance boundaries
Faster onboarding for Kafka users
MCP security
Does Axual MCP meet security team standards
Same permissions, same boundaries: AI operates with the exact permissions of the user. No elevation, no hidden access.
No direct Kafka access: All actions go through Axual Self-Service APIs: governance, ownership, and approvals remain enforced.
Your data stays in your platform: MCP interacts with Axual, not Kafka directly. No uncontrolled data exposure.
Works with enterprise constraints: Fully integrated with your Identity Access Management system.
Frequently asked questions
What is the Axual MCP Kafka server?
The Axual MCP Server connects AI assistants to the Axual platform, letting you manage Kafka using natural language instead of raw commands or UI clicks. It works through Axual's Self Service APIs, so every action such as creating topics, deploying apps, or browsing messages stays governed by existing ownership, environment, and access rules.
How does the Axual MCP Kafka server work?
You describe what you want in plain language, for example "create a topic called orders in dev," and the AI selects the right MCP tool to carry out the request. The server executes it through Axual's Self Service APIs, which enforce ownership, environment, and access policies before returning the result in the same conversation.
Is the Axual MCP Kafka server secure? Does it create new access for AI?
No, the MCP server doesn't grant any new access; it only makes a user's existing Axual permissions accessible via AI. The AI operates with the exact same permissions as the user (no elevation), never touches Kafka directly, and all actions pass through Axual's governed Self Service APIs, fully compatible with enterprise Identity and Access Management systems.
Can my AI agent create and deploy Flink SQL jobs?
Yes. Through the Axual MCP server, your AI assistant can create and manage Flink SQL applications. Describe the job in plain language, and the agent writes the SQL and deploys it as a managed job on a Flink cluster your tenant has registered. Flink SQL support is currently in preview.
Do I need to write Flink table definitions myself?
No. Axual can generate the table definitions from your connected Kafka topics, so you or your agent only need to write the transformation logic. You can still write the tables by hand if you prefer.
What can I do with the Axual MCP Kafka server?
You can discover and search topics, environments, and deployments; create topics with correct ownership; produce and browse test or real messages; create and manage streaming applications; generate and deploy KSML apps; and request or enforce Kafka access permissions, all through natural language prompts.
What is KSML and how does the Axual MCP server use it?
KSML (Kafka Streams Markup Language) lets you define Kafka Streams pipelines in YAML to filter, transform, and route data without writing code. Through the MCP Server, you can ask an AI assistant to generate a KSML application, register it, and deploy it directly on Axual, for example filtering pending transactions from a topic into another one.
Should I use KSML or Flink SQL?
Both run as managed applications on Axual. KSML suits teams that want to define Kafka Streams pipelines in YAML with Python logic, without running extra infrastructure. Flink SQL suits teams that already know SQL or run Flink, and want to process streams on their own Flink cluster. Your agent can work with either.
How do I connect a client to the Axual MCP server?
First, get your MCP server URL from Axual Self Service. Then connect your AI client. For example, with Claude Code you'd run claude mcp add --transport http axual [your-mcp-server-url]. Once connected, you can start asking questions like "What environments are available in Axual?"