CHATBOTS

Discord Bot: Suggest a Fix for My Failed MR

An agent-driven Discord bot that reads a failed GitLab MR's logs, queries an internal knowledge-base MCP for matching past incidents.

CategoryChatbots
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDiscord mention asking for fix help on an MRDiscordDiscord
  • ActionFetch failing trace and extract error signature from GitLabGitLabGitLab
  • ActionQuery internal knowledge-base MCP for matching incidentsCustom MCP server
  • LogicAgent synthesizes fix grounded in retrieved runbooks
  • OutputReply with suggested fix and matched incident in DiscordDiscordDiscord

What it does

This goes beyond explaining the failure — it proposes a fix. When a contributor asks about a failed MR, the agent pulls the failing trace from GitLab, searches an internal custom MCP server (your runbooks and past incident notes) for similar failures, and synthesizes a concrete suggested fix grounded in how the team solved it before.

When to use it

Use this when your CI failures map to a recurring set of known causes (missing migration, stale lockfile, env var drift) that are documented somewhere. It turns tribal knowledge into a self-serve answer and cuts repeat questions to senior engineers.

How it works

  1. 1A contributor mentions the bot in Discord with their MR ID and asks for help.
  2. 2The agent fetches the failing job trace from GitLab and extracts the error signature.
  3. 3It queries the internal knowledge-base MCP for past incidents matching that signature.
  4. 4The agent reasons over the trace plus retrieved runbooks to draft a specific fix.
  5. 5It replies in Discord with the suggested fix, the matched prior incident, and a confidence note.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Connect Custom MCP serverConnect any MCP-compatible tool you own.
  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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