minicheck-mcp

A model checker as an MCP server. Let the agent verify the state machine instead of guessing.

Why this exists

Agents design state machines constantly — retry loops, lock protocols, session lifecycles, hand-off between sub-agents — and then reason about correctness in prose. Prose reasoning about concurrency fails the same way for a model as it does for a person: by considering the interleavings that come to mind and missing the one that doesn't.

An agent with a decision procedure does not have to guess. It gets a verdict and, when the property fails, the exact sequence of steps that breaks it — which is also the thing it needs in order to fix the design rather than apologise for it.

The spec it sends is data, never code, so nothing the agent submits is executed — and what comes back is a verdict with a shortest counterexample trace.

Install

# from GitHub (PyPI release pending)
pip install "minicheck-mcp @ git+https://github.com/nickharris808/minicheck-mcp.git"
pip install "minicheck-mcp[mcp] @ git+https://github.com/nickharris808/minicheck-mcp.git"  # + the MCP SDK

pip install minicheck-mcp does not work yet — the package is not on PyPI. Install from GitHub as shown above; that pulls in minicheck automatically. python build_pypi.py produces a PyPI-uploadable artifact for when both packages are published (PyPI rejects the direct dependency reference this package uses to stay installable without an index).

Then register it (claude_desktop_config.json, or any MCP client):

{ "mcpServers": { "minicheck": { "command": "minicheck-mcp" } } }

The repo ships this as mcp.json.

30-second quickstart

Ask the agent: "I have a retry loop that increments a counter until it succeeds. Check that it can't retry more than 3 times." It calls check_invariant and gets back:

{
  "ok": true,
  "invariants": {
    "bounded_retries": {
      "holds": false,
      "steps": 4,
      "counterexample": [
        {"label": null,      "state": {"tries": 0, "done": 0}},
        {"label": "attempt", "state": {"tries": 1, "done": 0}},
        {"label": "attempt", "state": {"tries": 2, "done": 0}},
        {"label": "attempt", "state": {"tries": 3, "done": 0}},
        {"label": "attempt", "state": {"tries": 4, "done": 0}}
      ]
    }
  }
}

Not "this might loop forever" — the exact four steps that break it.

Tutorial — what a session actually looks like

The agent has written a session lifecycle and wants to know whether a session can be used after it has been closed. Here is the whole exchange.

1. The agent asks for the format (spec_help), then sends check_invariant:

{
  "name": "session",
  "fields": ["state", "used"],
  "initial": {"state": 0, "used": 0},
  "transitions": [
    {"label": "open",  "when": {"state": 0}, "set": {"state": 1}},
    {"label": "use",   "when": {"state": 1}, "set": {"used": 1}},
    {"label": "close", "when": {"state": 1}, "set": {"state": 2}},
    {"label": "reopen","when": {"state": 2}, "set": {"state": 1}}
  ],
  "invariants": {"no_use_after_close": {"forbid": {"state": 2, "used": 1}}}
}

2. It gets a refutation with the exact path:

{
  "ok": true,
  "verdict": "REFUTED",
  "exhaustive": true,
  "reachable_states": 5,
  "all_hold": false,
  "invariants": {
    "no_use_after_close": {
      "holds": false,
      "steps": 3,
      "counterexample": [
        {"label": null,    "state": {"state": 0, "used": 0}},
        {"label": "open",  "state": {"state": 1, "used": 0}},
        {"label": "use",   "state": {"state": 1, "used": 1}},
        {"label": "close", "state": {"state": 2, "used": 1}}
      ]
    }
  }
}

The invariant as written forbids ever having used a closed session, which is not what the agent meant — it meant "no use transition while closed". The counterexample makes the difference concrete rather than leaving it to a plausible-sounding paragraph.

3. The agent fixes the model and re-runs. used should mean "used since this session opened", so close clears it:

{"label": "close", "when": {"state": 1}, "set": {"state": 2, "used": 0}}

{"ok": true, "verdict": "PROVED", "exhaustive": true, "reachable_states": 4, "all_hold": true}

PROVED and exhaustive: true is the pair to read. The first cannot be issued without the second, but checking both makes the habit explicit — and the habit is what protects you on the day a spec grows past the bound.

4. What the agent must not do. If the reply is "verdict": "UNDETERMINED", that is not a pass. It means the search stopped early — read incomplete_reason and advice, bound the growing field, and ask again. If ok is false, no verdict exists at all and all_hold is null.

Tools

Tool	What it does
check_invariant	Exhaustive reachability. Shortest counterexample when a property fails.
check_liveness	Every reachable state can still reach the goal (AG-EF) — catches a state you can enter and never leave, which plain reachability misses.
validate_spec	Schema check without running it; the error names the offending key.
visualise	A Mermaid state diagram with the counterexample highlighted and its steps numbered — renders directly in GitHub Markdown, so an agent can show a user why rather than describe it.
spec_help	The format, with a worked example and its actual verdict.

The spec format

{
  "name": "mutex",
  "fields": ["a", "b", "lock"],
  "initial": {"a": 0, "b": 0, "lock": 0},
  "transitions": [
    {"label": "a_enter", "when": {"a": 0, "lock": 0}, "set": {"a": 1, "lock": 1}},
    {"label": "a_exit",  "when": {"a": 1},            "set": {"a": 0, "lock": 0}}
  ],
  "invariants": {"not_both": {"forbid": {"a": 1, "b": 1}}},
  "goal": {"require": {"a": 1}}
}

when is a conjunction of field == value tests (omit it for always-enabled). set assigns a literal, or {"incr": n} / {"decr": n} for integers. An invariant is {"forbid": {...}} (fails when every listed field matches) or {"require": {...}} (fails unless they do).

Integers are bounded, and the bound is checked — int_bound (default 64) is the largest magnitude a field may hold. A run that would carry a field past it stops and reports exhaustive: false rather than saturating the value, because a silently truncated search reports "holds" for states it never visited. See Honest scope for how to read the resulting verdict.

Why declarative

An MCP server that exec'd agent-supplied Python would be a remote code execution hole with extra steps. Specs here are data: a field value that looks like __import__('os').system(...) stays a string and is compared as one. There is a test that asserts exactly that.

No SDK? Still usable.

The tools are plain functions. dispatch is the same entry point the transport uses, so you can call it from a script or a test without an agent in the loop:

from minicheck_mcp import dispatch
dispatch("check_invariant", {"spec": my_spec})

Without mcp installed, minicheck-mcp prints a JSON error explaining how to install it and exits non-zero, rather than traceback-ing.

Honest scope

Read the verdict as three-valued. This is the part that matters most for an agent, because an agent reads a field and acts on it rather than bringing judgement to a paragraph.

all_hold	verdict	meaning
true	PROVED	every reachable state was enumerated; nothing violated the invariant
false	REFUTED	a counterexample is attached and it replays against your spec
null	UNDETERMINED	the search did not finish. Not a pass.
null	ERROR	with ok: false — no verdict was produced at all

Every response also carries verdict_means, a one-line explanation an agent can quote to a user verbatim rather than paraphrasing (and possibly softening) it.

Every response carries all_hold and holds explicitly, including errors. An earlier version omitted them on failure, so result.get("all_hold") returned None for a crash and for a genuine undetermined result alike — and both are falsy, exactly like a refutation.

When exhaustive is false, the response also carries incomplete_reason and advice naming what to change. A warnings array appears when an invariant is trivially satisfied — it genuinely holds, but verifies nothing.

What it proves. That a finite declarative state machine does or does not satisfy an invariant over every interleaving, within the declared bounds.

What it does not prove.

• Nothing about your implementation — only about the spec you sent. A spec abstracts.
• Nothing outside int_bound (default 64) or the 200,000-state cap. Exceeding either yields UNDETERMINED, never a silent pass.
• Nothing about liveness beyond AG-EF, and nothing in LTL.

Nothing in a spec is ever executed. A spec is data: field names, literals, and comparisons. There is no eval, no exec, and no code path that turns a string in a spec into a callable. That is why the declarative loader exists rather than accepting Python.

What is not here

This is the engine and a safe way to call it. The maintained hazard-property corpora, the composition analysis that finds hazards which exist only when two components are combined, and the evidence trail that makes a verdict auditable afterwards are the commercial offering. This server is MIT and stays that way.

Troubleshooting

ok: false, error: "SpecError". The spec is malformed and the message names the key. Call validate_spec first, or spec_help for the format with a worked example.

verdict: "UNDETERMINED" on a spec I expected to pass. The search did not cover the whole state space — usually a field that grows without bound. Read incomplete_reason and advice. Add a when guard that stops the growth. Do not treat this as a pass.

ok: false, error: "BadArguments". The tool was called with an argument it does not take. Every tool takes spec; check_invariant also takes an optional invariant name.

ok: false on check_liveness with "spec declares no 'goal'". Liveness needs something to reach. Add a goal block in the same shape as an invariant.

A warnings array appeared and the invariant still says holds: true. The invariant names a value the bounded space cannot represent, so it is satisfied for a reason unrelated to your protocol — usually a typo in the literal, or an int_bound below the value you meant to forbid.

The server exits immediately with a JSON error. The MCP SDK is not installed: pip install "minicheck-mcp[mcp] @ git+https://github.com/nickharris808/minicheck-mcp.git". The tools remain importable and testable without it via from minicheck_mcp import dispatch.

My agent treats an error as "the property is fine". It should not be able to: every response carries all_hold and holds explicitly, and both are null on any error, alongside verdict: "ERROR". Branch on result["ok"] first.

Performance

Bounded by the underlying checker. Specs arrive here declaratively, which is the checker's compiled path — roughly 2.5×10⁵–7.5×10⁵ states/second in CPython 3.11 on an M-series laptop, reproducible by running python bench.py in the minicheck repository. A spec that fits in a few tens of thousands of states answers in well under a second. There is no measured bottleneck in the server layer itself — it is a thin dispatch.

Tests

pip install -e ".[test]" && pytest

97 tests, every tool through the real dispatch path, including malformed input, unknown tools, and the no-code-execution guarantee.

The portfolio

Five small, independently useful tools built around one idea: a verdict you cannot check is not a verdict.

minicheck	An explicit-state model checker with a CLI. Shortest counterexamples, no required dependencies.
protocol-bench	15 published IEEE 802.11 / 3GPP procedures with ground truth. A claimed detection must replay.
minicheck-mcp ← you are here	The checker as an MCP server — let an agent verify a state machine instead of guessing.
polyfrac	Exact polynomial + rational-function arithmetic over ℚ with Sturm real-root counting. Zero deps.
failclosed	Default-deny ASGI middleware: a gated endpoint succeeds only on an affirmative verdict.
protocol-bench-action	Score a submission in CI and fail the build if a claimed detection cannot be proved

Try it in your browser: live demo · Ground-truth tasks: dataset

The commercial offering

These are the engine. What is not open source is what makes it useful at scale: the maintained hazard-property corpora, composition analysis that finds hazards existing only when two components are combined, the trust-model sensitivity sweep, and the evidence trail that makes a verdict auditable after the fact. The tools above are MIT and stay that way.

Licence

MIT. See LICENSE.