mcp-server-celery
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- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to diagnose a Python backend stack — Postgres, Celery, Redis and logs — in a single tool call, returning findings, a merged timeline of events, and possible cause-effect chains. All checks are strictly read-only, so an assistant can inspect blocked queries, missing workers, queue backlogs, Redis memory pressure and log error spikes without ever modifying anything.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to inspect, debug, and operate Redis Queue (RQ) queues, jobs, and workers, with access gated by read-only, write, or admin modes.12MIT

JMSToolBox MCP Serverofficial
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- AlicenseNot gradedqualityCmaintenanceGives AI agents read-only access to production NATS and JetStream event buses through twenty-five bounded, schema-typed tools covering message search, dead-letter inspection, causal flow reconstruction from a request or correlation id, and stream, consumer, KV, object-store and server-health topology. Because the runtime is wired only to read paths and every result is limited, cursored and truncation-flagged, agents can debug live traffic without any ability to mutate it.Apache 2.0
TDQS
Scored across 2 tools
The two tools target distinct resources: list_workers is worker-centric while queue_depths is queue-centric. There is minor overlap since both surface the worker-to-queue consumption mapping, but the primary purpose of each is clear.
Both names are snake_case, which is consistent. However list_workers follows a verb_noun pattern while queue_depths is noun_noun, a minor deviation in convention.
Two tools is thin for a Celery monitoring server, which typically covers tasks, queues, and workers. It is not extreme, but the surface feels under-scoped for the domain.
Celery monitoring spans workers, queues, and tasks, but only worker and queue visibility is offered. Task inspection (list tasks, task status, revoke) and queue management (purge) are notably absent, leaving significant gaps.