AI & RAG

On-Call Investigator with MCP Tool Grounding

An agent that takes a slash-command incident question in Discord, pulls live system context through a custom MCP server and the matching Confluence runbook.

CategoryAI & RAG
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerIncident slash command in DiscordDiscordDiscord
  • ActionGather live system context via MCPCustom MCP server
  • ActionRetrieve component runbook from ConfluenceConfluenceConfluence
  • LogicReconcile live data against runbook
  • OutputPost grounded diagnosis and next actionDiscordDiscord

What it does

Answers harder "what's actually happening right now" questions that a static doc can't. When an engineer triggers the incident slash command in Discord, an agent queries your internal systems through a custom MCP server (deploy state, feature flags, recent changes), cross-references the relevant Confluence runbook, and replies with a diagnosis grounded in both live data and documented procedure.

When to use it

Use it during active incidents when the answer depends on current system state, not just the playbook. Best for teams that have wrapped internal tooling behind an MCP server and want an agent to reason over live signals while staying anchored to approved runbooks.

How it works

  1. 1An engineer invokes the incident slash command in Discord with a question.
  2. 2The agent calls the custom MCP server to gather live system context.
  3. 3It retrieves the relevant runbook from Confluence for the affected component.
  4. 4A logic step checks whether live data and runbook agree or diverge.
  5. 5The agent posts a grounded diagnosis and recommended next action, citing both the runbook and the live signals it used.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  2. 2
    Connect Custom MCP serverConnect any MCP-compatible tool you own.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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