Queue AIops

Name: Queue AIops
Author: AIops-tools

by AIops-toolsversion 0.12.0i★ –iRuns on your computer

Registry nameio.github.AIops-tools/queue-aiops

Install

Overview

Description from the maker (original English)

Governed redis + rabbitmq ops: memory/latency/backlog/churn RCA, policies. 28 tools.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "queue-aiops": {
      "command": "uvx",
      "args": [
        "queue-aiops"
      ]
    }
  }
}

Run this in your terminal

claude mcp add queue-aiops -- uvx queue-aiops

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "queue-aiops": {
      "command": "uvx",
      "args": [
        "queue-aiops"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "queue-aiops": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "queue-aiops"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account AIops-tools, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7240 servers in this category
Published to the registry more recently than 83% of the other 7240 servers in this category
Basic checks equal to or better than 100% of the other 7240 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Queue AIops safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.12.0) reached the registry 7 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Queue AIops

Audit availability: Real writes require a committed pending audit row and stop before contacting the platform if storage is unavailable. Reads and previews may continue with a warning; new audit rows store outcome summaries rather than full responses.

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.

So there is no read-only switch, no policy file, no approval gate to configure. The one thing the tool guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.queue-aiops/audit.db, and reversible writes still capture their before-state and record an inverse.

Each tool declares a risk_level, kept in agreement with its [READ]/[WRITE]

documentation tag by a test, and carried into the audit row as a descriptive

tier — so a reviewer can see at a glance that a row was a high-risk delete. It

is a label, not a gate.

Running a smaller / local model? See agent-guardrails.md — it lists the guardrails this tool now enforces for you (so you don't spend prompt budget restating them) and gives a ready-made system prompt for what's left.

Support scope

Platform	Protocol	Coverage
redis (5.x–7.x wire protocol via `redis` Python client)	RESP, password optional, TLS optional	INFO (server/memory/clients/stats/persistence/keyspace), SLOWLOG, CLIENT LIST/KILL, CONFIG GET/SET, MEMORY STATS/USAGE, SCAN-budgeted big-key sampling, DBSIZE, PING
rabbitmq (management plugin HTTP API)	HTTP(S), Basic auth	/api/overview, /api/queues (+ per-vhost, detail, purge, declare, delete), /api/connections, /api/channels, /api/consumers, /api/policies (get/set/delete), /api/nodes

28 MCP tools — 20 reads (incl. 4 flagship RCAs) + 8 governed writes.

Group	Tools	R/W
Overview	queue_overview	read
redis reads	redis_server_info, redis_memory_stats, redis_clients, redis_slowlog, redis_config_get, redis_keyspace, redis_big_keys	read
rabbitmq reads	rabbitmq_overview, list_queues, queue_detail, list_connections, list_channels, list_policies, node_health	read
Flagship RCAs	redis_memory_pressure_rca, redis_latency_rca, rabbitmq_queue_backlog_rca, connection_churn_analysis	read
Writes (medium)	redis_config_set, redis_kill_client, declare_queue, set_policy, delete_policy	write
Writes (high)	purge_queue, delete_queue	write
Undo	undo_list, undo_apply	read / write

The four RCAs are transparent heuristics that report their numbers — thresholds are named constants, every finding carries its evidence, never a black-box verdict. Big-key sampling walks at most 10,000 keys with SCAN and sizes at most 200 with MEMORY USAGE — **never KEYS *** — and reports its coverage.

Governance

Every MCP tool — and every CLI write, which routes through the same governed functions — runs through the bundled @governed_tool harness (queue_aiops.governance — no external dependency). It records; it does not authorize whether a write may happen (see above):

Audit — every call lands in ~/.queue-aiops/audit.db (relocatable via QUEUE_AIOPS_HOME), secret-redacted. The CLI writes the same row the MCP path does — there is no unaudited entry point.
Budget / runaway guard — a safety backstop, not an authorization gate: call/time ceilings (QUEUE_MAX_TOOL_CALLS, QUEUE_MAX_TOOL_SECONDS) + a runaway-loop breaker stop a stuck agent from burning unbounded calls/time.
Risk tier — a descriptive label on the audit row derived from risk_level; it gates nothing. QUEUE_AUDIT_APPROVED_BY / QUEUE_AUDIT_RATIONALE are optional annotations recorded on the row (who/why), never required and never blocking.
Undo — reversible writes capture the real before-state first: redis_config_set records the prior value from CONFIG GET; set_policy/delete_policy record the prior policy; delete_queue records the queue's definition and its undo re-declares it (the messages are not restored — the descriptor says so). Irreversible writes (purge_queue, redis_kill_client) record priorState only.
Dry-run + double-confirm — every write takes dry_run=True (MCP) / --dry-run (CLI); CLI writes double-confirm and execute through the same governed twins, so they land in the audit log too. purge_queue and delete_queue are risk=high with dry-run + double confirmation at the CLI.
Credentials live encrypted in ~/.queue-aiops/secrets.enc (Fernet + scrypt master password; QUEUE_AIOPS_MASTER_PASSWORD for non-interactive use). Redis passwords are optional — an auth-less lab instance is a supported target.

MCP configuration

"mcpServers": { "queue-aiops": { "command": "uvx", "args": ["--from", "queue-aiops", "queue-aiops-mcp"], "env": { "QUEUE_AIOPS_MASTER_PASSWORD": "your-master-password" } } } }

env-block caveat: MCP clients launch the server with a *minimal*

environment — your shell profile is not sourced. Anything the server needs

(QUEUE_AIOPS_MASTER_PASSWORD, a relocated QUEUE_AIOPS_HOME, and any

optional QUEUE_AUDIT_APPROVED_BY/QUEUE_AUDIT_RATIONALE audit annotations)

must be set in the env block above, not just in your terminal.

Or, with the package installed: queue-aiops mcp.

CLI reference (short)

queue-aiops init | doctor | overview | mcp

Verification status

Live-verified against Redis 7.4.9 and RabbitMQ 3.13.7 (2026-07-19/20). Connectivity, every Redis read (cross-checked against redis-cli ground truth), all four analyses, and the full governance loop (real redis_config_set → audit row → undo restoring the prior value) were exercised against a real server. That run found and fixed a real defect: integer quantities — key counts, client counts, byte totals — were rendered as floats (202.0 keys), which equality assertions cannot catch.

The RabbitMQ group is now verified end-to-end too — reads cross-checked against rabbitmqadmin, and set_policy → undo_apply closing on the live broker. Redis cluster/sentinel topologies and AUTH/TLS connections remain unverified.

docs/VERIFICATION.md records exactly what was checked and what is still open. queue-aiops doctor is the fastest live check.

Contributing

缺功能提 issue/PR 欢迎留言 — missing a read you need (streams/consumer groups, quorum-queue specifics, shovel/federation status), another broker platform, or a threshold that doesn't fit your fleet? Open an issue or PR at github.com/AIops-tools/Queue-AIops — platform registry entries are additive and small.

License

MIT

Queue AIops

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Queue AIops

Quick start

As a Claude Code plugin

As an OpenClaw plugin

As a CLI or standalone MCP server

What this tool does, and does not, decide

Support scope

Governance

MCP configuration

CLI reference (short)

Verification status

Contributing

License