Read-only IBM MQ diagnostics for AI agents. Cited root cause, never destructive.
MQ-Sentinel (io.github.pramodreddyboddu/mq-sentinel) MCP Server
Read-only Model Context Protocol (MCP) server providing IBM MQ diagnostics for AI agents. The server is positioned for safety by focusing on non-destructive inspection and “cited root cause” reporting, including observability and security considerations for enterprise environments.
🛠️ Key Features
Read-only IBM MQ diagnostics for AI agents
Provides cited root cause information
Designed to be non-destructive (“never destructive”)
Topics include diagnostics, observability, and security
🚀 Use Cases
Enterprise IBM MQ issue diagnosis by AI agents
Root-cause oriented troubleshooting with supporting citations
Observability and diagnostics workflows in production environments
⚡ Developer Benefits
Safe infrastructure for AI agents (read-only behavior)
Supports enterprise observability and diagnostic use
Emphasizes security and production deployment
⚠️ Limitations
Limited to IBM MQ diagnostics (diagnostic scope stated; destructive actions not supported)
Production-grade, read-only IBM MQ diagnostic MCP server that AI agents can safely use — with zero hallucinations.
I built this as a serious side project to solve a real, high-stakes problem in enterprise environments. It is now a strong public portfolio piece demonstrating systems engineering, security, observability, production deployment, and building safe infrastructure for AI agents.
0.3.0 — 8 diagnostic tools across all 10 IBM MQ flavors. Read-only by design. Prompt-injection firewall. OIDC + RBAC. Hash-chained audit. Verified IBM Knowledge Center citations (CI-enforced). Production Helm with HPA + air-gapped packaging.
Enterprise IBM MQ teams waste hours diagnosing issues like:
2035 NOT_AUTHORIZED storms
Exploding DLQs
Native HA replica lag and split-brain
Stale cluster members
z/OS mysteries
Traditional tools require deep expertise and manual log diving. Handing an LLM raw MQ access is unacceptable for security and compliance teams.
MQ-Sentinel gives Claude, Cursor, Grok, and other agents a safe MCP interface that returns accurate, cited diagnostics without ever hallucinating or executing destructive commands.
The Problem
IBM MQ teams (banks, telcos, insurance, gov) waste hours on:
2035 NOT_AUTHORIZED channel storms
Exploding DLQs
Native HA replica lag / split-brain
Stale cluster members
z/OS QSG mysteries
Traditional tools require deep MQSC knowledge and grepping logs. Giving an LLM direct MQ access is a non-starter for security/compliance teams.
MQ-Sentinel solves this by giving Claude, Cursor, Grok, etc. a safe, read-only MCP interface that returns typed Root Cause + Fix Steps + verified IBM docs — never fabricates, never executes destructive commands.
🎬 See it in 90 seconds
bash
git clone https://github.com/pramodreddyboddu/mq-sentinel.git
cd mq-sentinel
make demo
Watch MQ-Sentinel diagnose a real 2035 NOT_AUTHORIZED, an INDOUBT channel, a 1247-message DLQ, and a Native HA replica disconnect — all against the bundled fixture sandbox. No live IBM MQ required.
Want to record it for sharing? make demo-record produces a browser-playable asciinema cast.
Quick self-check (no MQ needed):
bash
uv run mq-sentinel doctor
uv run mq-sentinel tools # list all diagnostics
uv run mq-sentinel info # overview + security highlights
Use it from Claude, Grok, Cursor, Gemini, ChatGPT
One MCP server — not a per-vendor plugin. Copy-paste configs: docs/mcp-clients.md.
List it where agents discover servers: official MCP Registry (server.json) + Smithery (smithery.yaml). ChatGPT needs a hosted HTTPS endpoint, not local stdio.
I built MQ-Sentinel to solve a painful, recurring problem in enterprise IBM MQ environments — and I treated the entire effort as a serious engineering and portfolio project.
I used Grok Build (xAI) as my primary coding partner from architecture through implementation, docs, Helm, compliance artifacts, and polish. Every major decision was deliberate: read-only first, citations that can't go stale, defense-in-depth, production packaging.
Key milestones:
Designed threat model + multi-layer security architecture before writing the first tool
Delivered 8 diagnostic tools across 10 MQ flavors with CI-enforced IBM Knowledge Center citations
Production deployment (Helm + HPA + ServiceMonitor, distroless, air-gapped RPM/DEB)
Full 7-phase org-readiness plan executed (see links above)
271+ tests (including security-negative tests), strict mypy, comprehensive docs for platform teams
This shows end-to-end ownership: deep domain knowledge, security by design, production operations, and shipping artifacts that real organizations can actually use.
This is my public portfolio project. It demonstrates systems engineering, security, observability, AI infrastructure, and disciplined delivery.
Built with Grok Build while holding high standards throughout.
Security posture — baked in, not bolted on
Read-only always. Static MQSC allowlist (DISPLAY / DIS / PING CHANNEL only). Destructive verbs are rejected by three layers: tool, connector, MQ-side setmqaut.
Prompt-injection firewall. Every MQ-sourced string is sanitized (control chars, zero-width, ANSI, unicode tag chars, jailbreak markers) and wrapped in a quarantine envelope before leaving the server.
URL allowlist + live verification. Responses may only cite www.ibm.com; all other URLs are redacted. Every citation in the registry (20+ reason codes, 8 AMQ codes, 18 topic pages) is fetched daily in CI — a dead link fails the build, not the customer.
make install # uv sync + editable install
make test# pytest
make ci # lint + type + tests + security suite (everything CI runs)
make docker # build the production container image
make rpm deb # build RPM + DEB packages (requires `gem install fpm`)
This project was deliberately taken through a complete org-readiness program (7 phases) so it is credible for real platform/SRE teams. See the full plan and status: