# Minecraft Debug MCP > Operate and debug the live Minecraft bot through its built-in MCP REPL server. Use when work requires starting the bot with `pnpm dev`, connecting to the local MCP endpoint, inspecting cognitive state/logs/history, injecting synthetic chat/events, or running targeted REPL code against the running brain during investigation and development. Use when this capability is needed. - Skill: `tomevault-io/minecraft-debug-mcp` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/minecraft-debug-mcp` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/minecraft-debug-mcp/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/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. --- > Source: [moeru-ai/airi](https://github.com/moeru-ai/airi) — distributed by [TomeVault](https://tomevault.io).