minecraft-debug-mcp

Minecraft Debug MCP

Overview

Use this skill to run the local bot and interact with its MCP debug interface safely and quickly.

Quick Start Workflow

1. Run pnpm dev from /path/to/project/root/services/minecraft and keep it running.
2. Wait for MCP REPL server running at http://localhost:3001 in logs.
3. Connect MCP client to http://localhost:3001/sse.
4. Verify readiness with a read-only call:
   • Read resource brain://state, or
   • Call tool get_state.
5. Continue with the smallest tool/action that answers the task.

Execution Rules

• Start read-only, then escalate to mutation tools only when needed.
• Prefer get_state, get_last_prompt, and get_logs for diagnostics before execute_repl.
• Prefer get_llm_trace for structured per-attempt reasoning/content inspection.
• Keep execute_repl snippets minimal and reversible.
• Use inject_chat for conversational simulation and inject_event only when specific event-shape testing is required.
• Treat inject_chat as side-effectful: it can trigger actual in-game bot replies/actions.
• If MCP connection fails, check that pnpm dev is still running and port 3001 is free.

Tooling Strategy

• Use get_state to inspect queue/processing state and available tools/actions (skips REPL builtins by default; pass { includeBuiltins: true } to include them).
• Use get_logs with a small limit first.
• Use get_last_prompt to inspect latest LLM input.
• Use execute_repl for deep object inspection or one-off targeted calls on the running brain.
• Use inject_chat to simulate player chat and verify behavior loop.
• Use get_llm_trace to assert REPL behavior in automation (for example, detect repeated await skip() on specific events).
• Use execute_repl("forget_conversation()") to clear conversation memory before prompt-engineering tests.

Read references/mcp-surface.md for exact tool/resource names and argument schemas.

Live-Tested Notes

• get_state returns available tools/actions and runtime state (skips REPL builtins like skip, use, log by default to reduce noise; pass { includeBuiltins: true } if you need to inspect them).
• get_last_prompt can return very large payloads; call only when prompt-level debugging is needed.
• execute_repl returns a structured result where returnValue is stringified; parse mentally as display output, not typed JSON.
• get_logs(limit=10) is enough to verify whether an injected event reached REPL/executor.
• get_llm_trace(limit, turnId?) gives structured attempt-level trace data (messages, content, reasoning, usage, duration).
• get_last_prompt and get_llm_trace are compacted for MCP: system prompt/system-role messages are omitted to reduce token cost.
• Prefer compact value reads in REPL:
  • query.self() for bot status.
  • query.inventory().has(name, n) / query.inventory().count(name) for checks.
  • query.inventory().summary() for stable aggregated item output.
  • query.snapshot(range?) for one-shot world+inventory capture.
• forget_conversation() is available as a runtime function in REPL/global context and clears only conversation memory.
• Current prompt behavior supports two-turn value-first flows: read/query turn returns concrete data first, follow-up turn performs chat/action using that returned value.

Live Testing Workflow

1. Confirm MCP health:
   • Call get_state.
2. Capture baseline inventory:
   • execute_repl with query.inventory().list().map(i => ({ name: i.name, count: i.count })).
3. Trigger a task through normal cognition path:
   • Call inject_chat with a clear instruction (example: "please gather 3 dirt blocks").
4. Verify execution trace:
   • Call get_logs(limit=10) and check for:
     • bot acknowledgement chat
     • action tool feedback (for example collectBlocks)
     • REPL result summary
   • Call get_llm_trace(limit=5) when you need exact model output/reasoning for assertions.
5. Re-check inventory using the same REPL snippet and compare against baseline.

Use this workflow when validating behavior changes, tool wiring, or regressions in planning/execution loops.