Queue AIops MCP server

<!-- mcp-name: io.github.AIops-tools/queue-aiops -->

Queue AIops

Governed AI-ops for redis + rabbitmq. queue-aiops is for the team running their own cache and message broker — a redis that "suddenly eats memory", a rabbitmq whose queues quietly grow until publishers block — without an enterprise observability suite. It gives an AI agent (or a human at the CLI) a governed toolset over both: transparent root-cause analyses for memory pressure, latency, queue backlog, and connection churn, plus the handful of writes an operator actually needs (config set, client kill, purge/delete queue, policies) — every call audited, budgeted, risk-tiered, and undo-recorded by the built-in governance harness.

Disclaimer: Community-maintained open-source project, **not affiliated

with, endorsed by, or sponsored by the Redis or RabbitMQ projects or their

respective owners.** Redis and RabbitMQ are trademarks of their respective owners.

Verification: behaviour is covered by a mock-based test suite; not yet validated

against live brokers. Both redis and rabbitmq are free/self-hostable (one lab container

or package install each), so a lab check is easy — queue-aiops doctor is the fastest

live probe, and docs/VERIFICATION.md is the checklist.

Quick start

As a Claude Code plugin

One install gives an agent both the skill and the MCP server:

/plugin marketplace add AIops-tools/marketplace
/plugin install queue-aiops@aiops-tools

The MCP server is fetched with uv and pinned to the package version this plugin declares, so an audit row can be traced back to the code that wrote it. Credentials are still configured with queue-aiops init — see below.

As an OpenClaw plugin

The same bundle is published on ClawHub, where one install delivers the skill and its MCP server together:

openclaw plugins install clawhub:@zw008/queue-aiops
openclaw skills info queue-aiops          # expect: Visible to model: yes

Restart the OpenClaw gateway afterwards so it loads the plugin. The MCP server is fetched with uv, pinned to this exact release, so uvx has to be on PATH — without it the skill still installs but reports Visible to model: no. Credentials are configured exactly as below.

As a CLI or standalone MCP server

uv tool install queue-aiops

queue-aiops init      # wizard: pick platform (redis/rabbitmq), host/port, encrypted secret
queue-aiops doctor    # config + secret + connectivity check (PING / /api/overview)
queue-aiops overview  # one-shot health summary for the default target

Then the interesting parts:

queue-aiops analyze memory     # redis memory-pressure RCA (maxmemory, eviction, frag, big keys)
queue-aiops analyze latency    # redis latency RCA (slowlog digest, fork/AOF stalls)
queue-aiops analyze backlog    # rabbitmq queue-backlog RCA (consumers, unacked, watermarks)
queue-aiops analyze churn      # connection churn, both platforms
queue-aiops redis bigkeys      # SCAN-budgeted big-key sample (never KEYS *)
queue-aiops rabbitmq queues    # deepest backlog first

What this tool does, and does not, decide

It delivers broker operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the account you connect it with: give the Redis connection an ACL user restricted to read commands, or the RabbitMQ management user only the monitoring tag, and the writes fail at the broker — the place that actually owns the permission.

Shortened. The full README is on GitHub.