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Conduktor MCP Server

Official
by conduktor

Conduktor MCP

Put an AI assistant in charge of your Kafka estate.

Not a chat window onto a cluster. A way for agents — yours, or ours — to see what is actually happening across every cluster you run, and to act on it: reclaim the topics nobody consumes, attribute cost back to the teams spending it, catch the certificate expiring next month, restart the connector that failed at 3am, label the topics whose owner left the company.

The MCP endpoint ships inside Conduktor Console. There is nothing to deploy, and nothing new to secure: the assistant inherits the RBAC, audit trail and ownership model you already run.

One endpoint for your tools and for Console agents. Every call runs under the caller's own RBAC and is audited. Metadata reaches the model; your records stay in Kafka.

Try it first

No Kafka to hand? demo/ brings up a cluster, Console and the MCP endpoint with docker compose up -d, seeded with topics, traffic and a consumer group that is deliberately behind.

Related MCP server: MCP Kafka

Setup

Create a Personal Access Token in Console, then point your MCP client at your own Console:

{
  "mcpServers": {
    "conduktor": {
      "command": "npx",
      "args": ["-y", "@conduktor/mcp"],
      "env": {
        "CONDUKTOR_CONSOLE_URL": "https://console.acme-corp.com",
        "CONDUKTOR_API_TOKEN": "your-personal-access-token"
      }
    }
  }
}

/api/mcp is appended for you. Pasting a URL that already ends in it works too.

@conduktor/mcp wraps mcp-remote. Call it yourself if you prefer:

{
  "mcpServers": {
    "conduktor": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://console.acme-corp.com/api/mcp",
        "--header",
        "Authorization: Bearer ${CONDUKTOR_API_TOKEN}"
      ],
      "env": { "CONDUKTOR_API_TOKEN": "your-personal-access-token" }
    }
  }
}

What an assistant can ask

Ask in English, get answers grounded in your actual estate rather than in documentation:

Which topics haven't been consumed in a month, and who created them? What did the payments team cost us last quarter, and what drove it? This consumer group is stuck — what's the blocking message? Which certificates expire before March, and whose are they? Nobody owns these topics. Can you work out who should, from the lineage?

Those are not demos. Each maps onto tools below, and onto tasks an agent can run unattended.

From answering to operating

The same catalogue serves two kinds of caller. Your tools — Claude Code, Cursor, scripts, your internal developer platform — and agents running inside Console, on a schedule or on an audit-log event, under their own machine identity.

That second one is where this stops being a chatbot. An agent is a task plus an identity plus a bounded set of tools. It wakes up on Monday at 9am, or the moment a connector fails, works the problem, and comes back with something you can act on — a report, a recommendation, or a mutation it proposes and you approve.

Every run is traced end to end: the prompt, each tool call, the tokens, the result. That is what you attach to a ticket, or hand to an auditor.

Tools

Thirty-one, across the whole control plane. Most read; some write, and that set is growing.

Clusters

Tool

What it does

list-clusters

Every Kafka cluster this Console manages, with its Schema Registry, Connect clusters and flavor

get-cluster

One cluster's registration by slug

insights-cluster

Health score, topic and partition counts, serialization breakdown — the shape of a cluster in one call

Topics

Tool

What it does

list-topics

Topic catalogue: names, labels, descriptions, partitions, replication, configs

get-topic

One topic by exact name

list-topics-with-usage

Topics with message count, size and throughput

query-topics

Filter topics across a cluster

aggregate-topics

Group topics and compute a statistic per group — count, sum, average, min, max

insights-topics

Partition skew, replication problems, and what is quietly wrong

set-topic-labels (writes)

Label topics — ownership, environment, whatever your taxonomy is

Messages

Tool

What it does

get-last-messages

The last N messages from a topic

get-record-at

A specific record, by partition and offset — the one blocking a consumer

Consumer groups

Tool

What it does

list-consumer-groups

Groups with state, lag and member count

get-consumer-group

One group in detail

list-consumer-groups-by-topic

Who actually reads this topic

Schemas

Tool

What it does

list-subjects · get-subject · list-subject-names

The subject catalogue

get-schema-version

A specific version

check-schema-compatibility

Whether a change breaks consumers, before it ships

Connect, Gateway

Tool

What it does

list-connectors-detailed · get-connector

Connectors and their state

list-interceptors

Gateway interceptors configured on a cluster

Access, identity, governance, cost

Tool

What it does

list-acl-bindings

Who is allowed to do what

list-service-accounts · get-service-account

Non-human identities

list-certificates

Certificates and their expiry

list-applications

Applications registered in the catalogue

get-stream-lineage

What flows into what

query-audit-log

Who did what, when

get-chargeback-report

Cost attributed per team

Security is what makes this possible

Giving an agent real access to production is only reasonable because the boundaries already exist and are enforced per call.

It is you. Every call carries a Console token and runs under that user's RBAC. An agent cannot see a cluster its owner cannot see. Revoke the owner's access and the agent loses it at the same instant.

Control plane, not data plane. What reaches the model is metadata — topics, configs, offsets, groups, schemas, lineage, cost. Your records stay in Kafka unless a task explicitly needs a tool that reads them.

Everything is audited. Every call lands in the Console audit log, attributable to the identity that made it.

Documentation

What is in this repository

The launcher published as @conduktor/mcp, this documentation, and the registry metadata. The server itself runs inside Console. For bugs in the server use Conduktor support; for the launcher or these docs, open an issue here.

Related MCP Connectors

  • Query your org's data in natural language — read-only MCP access to SQL, NoSQL, files & warehouses.

  • The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.

  • Query your warehouse or a CSV with Claude/ChatGPT over MCP, governed by table-level ACL + audit.

  • Talk to your public-facing AI from any MCP client — Claude, ChatGPT, Cursor, Cline, Windsurf.

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