The io.github.AIops-tools/queue-aiops Model Context Protocol (MCP) server provides governed Redis + RabbitMQ operations for AIOps tasks. It focuses on memory/latency/backlog/churn RCA and policies, and exposes 26 tools for interacting with queue-related operational signals.

🛠️ Key Features

Governed Redis + RabbitMQ operations
RCA for memory, latency, backlog, and churn
Policies for governing operational behavior
26 tools available via MCP

🚀 Use Cases

Performing root-cause analysis for queue performance and stability
Tracking queue health signals: memory, latency, backlog, churn
Applying governance policies for Redis/RabbitMQ operational workflows

⚡ Developer Benefits

Standardized access to 26 MCP tools for queue AIOps operations
Tooling targeted to specific operational RCA dimensions: memory/latency/backlog/churn
Policy support to align investigations and actions with governance needs

⚠️ Limitations

Scope is limited to Redis + RabbitMQ queue operations and related RCA/policy needs.

Queue AIops (preview)

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.

Preview / mock-only: not yet validated against production 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.

Quick start

bash

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:

bash

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

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

26 MCP tools — 19 reads (incl. 4 flagship RCAs) + 7 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

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 runs through the bundled @governed_tool harness (queue_aiops.governance — no external dependency):

Audit — every call lands in ~/.queue-aiops/audit.db (relocatable via QUEUE_AIOPS_HOME), secret-redacted.
Budget — call/time ceilings (QUEUE_MAX_TOOL_CALLS, QUEUE_MAX_TOOL_SECONDS) + a runaway-loop breaker.
Risk tiers & approval — secure by default: with no ~/.queue-aiops/rules.yaml, high-risk writes (purge_queue, delete_queue) are denied unless QUEUE_AUDIT_APPROVED_BY names an approver (set QUEUE_AUDIT_RATIONALE too). queue-aiops init seeds a starter rules.yaml with that dual-control tier; an operator-authored rules file is honoured as-is.
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.
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

json

{
  "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, QUEUE_AUDIT_APPROVED_BY for high-risk writes) must be set in the env block above, not just in your terminal.

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

CLI reference (short)

text

queue-aiops init | doctor | overview | mcp
queue-aiops secret set|list|migrate ...
queue-aiops redis info|memory|clients|slowlog|config-get|keyspace|bigkeys
queue-aiops redis config-set <param> <value> [--dry-run]
queue-aiops redis kill-client --id <id> | --addr <ip:port> [--dry-run]
queue-aiops rabbitmq overview|queues|queue|connections|channels|policies|nodes
queue-aiops rabbitmq purge|delete-queue|declare-queue <name> [--vhost /] [--dry-run]
queue-aiops rabbitmq set-policy|delete-policy <name> ... [--dry-run]
queue-aiops analyze memory|latency|backlog|churn

Verification status

Preview / mock-only: the full test suite runs against mocked clients (no live broker needed), and the REST paths / INFO fields are modelled from the public docs of both platforms. Not yet validated against live production brokers — both are trivially self-hostable, so queue-aiops doctor against a local lab redis instance / rabbitmq broker (management plugin enabled) is the quickest 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