Kafka Sentinel

kafka-sentinel-mcp

[CI] [PyPI] [Python] [License: MIT]

Give AI agents safe, read-only eyes on your Kafka clusters.

An MCP (Model Context Protocol) server that exposes Kafka cluster health, consumer lag, partition state, and replay-readiness as structured tools — so LLM agents (Claude, or any MCP client) can diagnose streaming incidents without ever being able to break anything.

Built by an engineer who spent a decade running Kafka-based financial messaging at 99.999% availability, and got tired of every "AI + Kafka" demo assuming write access to production.

Why this exists

When a consumer group stalls at 3 a.m., the questions are always the same: Is it lag? A stuck partition? A rebalance storm? An offset reset gone wrong? These are pattern-matching questions — exactly what LLM agents are good at — but no operator will hand an agent admin rights on a production cluster.

kafka-sentinel-mcp draws a hard line: every tool is read-only by design, enforced at the client-config level (no admin operations are even imported). The agent can observe, correlate, and recommend; a human executes.

Tools

Quick start

pip install kafka-sentinel-mcp   # (or: uv tool install)
# Run against your cluster (read-only credentials!)KAFKA_BOOTSTRAP=localhost:9092 kafka-sentinel-mcp

Add to Claude Desktop / any MCP client:

{  "mcpServers": {    "kafka-sentinel": {      "command": "kafka-sentinel-mcp",      "env": { "KAFKA_BOOTSTRAP": "broker1:9092,broker2:9092" }    }  }}

Then ask your agent: "Why is the payments-consumer group falling behind, and can we still replay from where it stalled?"

Security posture

• Read-only by construction: no produce, no topic/config mutation, no offset commits, no ACL ops. The mutation APIs are never imported, and a test in CI greps the server source on every run to keep it that way.

• The observer consumer runs with enable.auto.commit=False and never commits — verified against a real broker, not just asserted.

• Supports SASL/SSL; credentials are read from the environment only and never logged.

• Every tool call is logged with its parameters for audit.

• Least privilege: run with a principal that has only Describe on the cluster and topics, and Describe on consumer groups. When an ACL denies an operation the tool returns a structured result rather than a stack trace:

  json

  {  "error": "permission_denied",  "operation": "list_consumer_groups",  "detail": "...",  "hint": "The Kafka principal in use lacks the ACL required for this operation. ..."}

  The agent can then tell the operator which ACL is missing instead of appearing broken. Non-authorization failures are deliberately not swallowed — they propagate, because silently degrading on an unrelated error would hide real problems.

Testing

pip install -e ".[dev]"
pytest -m "not integration"   # fast, fully mocked — no Docker neededpytest -m integration         # starts a real Kafka via testcontainers (needs Docker)pytest                        # both

The unit suite mocks librdkafka entirely and covers tool logic. The integration suite starts an actual broker, produces real records, and asserts the tools return correct lag, ISR state, durability flags, and replay-readiness — including that the observer leaves no committed offsets behind. Both run in CI.

Status

Early but tested. See ROADMAP.md. Issues and PRs welcome — especially war stories about what you wish an agent could have told you during an incident.

Citing this work

If you reference this project in academic work, see CITATION.cff, or use the "Cite this repository" button on GitHub.

License

MIT