dmcheck-mcp

dmcheck-mcp

MCP server for refereeing tabletop RPG sessions by analyzing transcripts against a configurable charter, providing deterministic findings on rule violations like unanswered questions or unacknowledged dice rolls.

Category
Visit Server

README

dmcheck

Deterministic conduct verdicts for live tabletop sessions — CI for running a game.

Feed it a session transcript (and optionally an engine event ledger) plus a table charter, and it returns named findings — the player whose question was never answered, the dice roll nobody acknowledged, the turn that began without anyone being told, the spoiler that leaked into the channel, the five-minute dead air. Every finding cites the charter rule it violates, with the evidence attached.

Cold-boot probe (2026-07-24): a fresh agent session given only this repo URL installed and refereed a session in 2 commands, verified all three exit-code legs against the docs, and confirmed the no-false-accusation contract held (a consumed roll produced silence). Its friction notes shipped as 0.1.1 (--dice-bot, dmcheck charter).

The design contract: a false accusation is the unforgivable bug. A rule fires only when the transcript provably shows the violation — ambiguity produces silence, never noise. The verdict path is model-free and deterministic: same transcript, same findings, every time.

30 seconds to a refereed session

$ pip install dmcheck          # stdlib only, no dependencies
$ dmcheck run session.jsonl --gm "Greta"
{
 "messages": 9,
 "findings": [
  {"rule": "R2", "summary": "unconsumed-roll: a dice result was never followed by any GM message",
   "charter": "roll_ack_within_messages=4",
   "detail": "dice result from DiceBot never followed by a GM message",
   "evidence": {"index": 6, "author": "DiceBot", "content": "Bram rolls 1d20+4: [18] = 22"}},
  ...
 ],
 "counts": {"R1": 1, "R2": 1, "R6": 1, "R7": 1, "R8": 1}
}

Transcript formats: JSONL of {ts, author, content}, or a JSON array of Discord-API-shaped messages ({timestamp, author: {username}, content}) in either order.

The rule set (each one paid for by a real table failure)

Rule Fires when Origin story
R1 a player's question got no GM response within threshold a player asked the DM a lore question; another player ended up answering
R2 a dice result was never followed by any GM message "did I hit?" — a player's successful attack roll sat unacknowledged
R3 an engine event was never narrated to the table the state engine resolved a hit the table never heard about
R4 a turn began and the GM never addressed the actor by name "isn't it her turn?" — asked by a player, which is one player too many
R5 someone acted out of initiative (needs the ledger) engine rejected it silently; the table never knew
R6 a configured hidden term appeared in a GM message a module's secret state names leaked into narration
R7 GM dead air beyond threshold while a player waited 30 seconds reads as thinking; five minutes reads as absence
R8 the session ended with open R1–R3 findings in its tail sessions should end in a defined state — that's what makes the next one possible

These came from running a hybrid table — human and AI players, an AI GM — on Discord, where every one of these failures actually happened and got codified the same week. They apply equally to human GMs: run dmcheck over your own exported game log and see what your table's transcript says.

Live mode (0.2): the referee sits AT the table

dmcheck watch runs the same engine over a growing session — stdin JSONL or a tailed file — and emits lifecycle events: OPEN when a violation becomes provable (thresholds fully elapsed; no predictions, ever), RESOLVED when a living condition heals (the engine event finally got narrated). --notify-cmd fires your own hook per OPEN finding — dmcheck itself never posts anywhere. At session end a closed-mode pass runs, so watch's final state provably equals dmcheck run on the full transcript (tested).

$ your-chat-fetcher | dmcheck watch - --gm "Rob" --notify-cmd 'notify-dm.sh'
{"event": "open", "rule": "R2", "detail": "dice result from RollBot never followed by a GM message", ...}
{"event": "resolved", "rule": "R3", ...}
{"event": "session_end", "open": ["R1"], "open_count": 1}
$ dmcheck explain R2            # the rule, its charter knobs, and the table failure that earned it
$ dmcheck lint-charter my.json  # unknown keys / bad thresholds refuse loudly

The point of live: every failure the rules encode was recoverable in the moment it happened — the unanswered question, the stale roll, the missing cue all had a seconds-wide window where a nudge saved the beat. Post-hoc tells you what went wrong last night; watch taps the GM's shoulder before the player feels it.

The charter is config, not code

charters/default.json ships thresholds and conventions derived from a real table's protocol. Override any of it — cue conventions, dead-air tolerance, dice-bot names, hidden-term lists — and version it. A league or organized-play program could publish a charter the way they publish a player's guide; dmcheck then referees any table against it.

$ dmcheck run session.jsonl --charter our-table.json --ledger events.jsonl
$ dmcheck rules            # the rule set with definitions
$ dmcheck charter          # print the effective charter — copy, edit, version it
$ dmcheck run session.jsonl --gm "Rob" --dice-bot "RollBot"   # quick overrides, no file needed
$ dmcheck --schema         # machine-readable I/O contract

For agents

  • tool.json at the repo root; --schema; exit codes: 0 clean · 1 findings · 2 charter/input unusable.
  • MCP server: dmcheck-mcp (stdio) with tools run and rules.
  • Findings are structured JSON with rule id, charter citation, human-readable detail, and an evidence span — built to be consumed by a GM agent that fixes its own procedure between beats.

What it does NOT do (on purpose)

  • No rules adjudication — whether the attack was legal is srdcheck's job.
  • No character math — that's charactercheck. (srdcheck judges the rules, charactercheck derives the actor, dmcheck referees the table.)
  • No narrative-quality judging — whether the prose was good is taste, and taste is not checkable. dmcheck checks procedure only.
  • No model calls, no scores — deterministic findings per rule, never a blended "DM grade."

Credits

The rule set was distilled from live hybrid (human + AI) table sessions; the Router+Detector pattern in native-gaming-harness independently converged on the same idea, which we take as evidence it's the load-bearing piece. dmcheck is game-system-agnostic and unaffiliated with any publisher.

<!-- MCP registry ownership marker (do not remove): binds this repo's PyPI package to its registry namespace. --> mcp-name: io.github.chaoz23/dmcheck

Bootstrap a new table (v0.3)

dmcheck init charter.json --gm YOUR-NAME

Writes a starter charter (versioned, effective-dated, lint-clean by construction) and prints the session-zero checklist S1–S8 — including S3c: sheet accountability is declared out loud at session zero, then settlement quizzes are graded silently. The referee that judges your table also hands you its constitution.

Per-seat cue policy (R4, hardened). Agent seats behind mention-gated transports (e.g. Discord allowBots="mentions") never receive name-in-prose cues. Declare it:

"seats": {"Shalia": {"cue_requires_mention": true, "mention": "<@1493...>"}}

R4 then counts a cue only if the literal mention string is present. Origin: a live session where "Shalia — you're up" was posted, looked like a cue, and was provably undeliverable — R4 passed on it. Never again.

Ledger format (the declared standard). dmcheck's ledger is JSONL: {ts, type: turn|act|event, actor, text} — one line per engine event. No lightweight OSS session-ledger existed when we surveyed (2026-07-26), so this format is the interchange standard our stack shares: engines tap their logs into it; run, watch, and settlement all consume it.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured