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chase-mcp

MCP

Enables AI agents to write, compose, and check assume-guarantee contracts, including refinement, consistency, compatibility, realizability, and LTL checks, and to use returned counterexamples to fix specifications.

@chase-cps · BSD 3-Clause · updated today

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git clone https://github.com/chase-cps/chase-mcp.git

See the README to configure this MCP server

README

CHASE MCP server

This repository contains a Model Context Protocol (MCP) server and an agent skill for CHASE (Contract-based Heterogeneous Analysis and Systems Exploration).

The server exposes the contract operations of CHASE as tools that an AI agent can call: an agent can write assume-guarantee contracts, compose them, check refinement, and use the counterexamples returned by the model checker to fix the specification. The skill tells the agent how to use these tools.

Tools

Contract operations that build contracts (each tool returns a new contract):

  • compose: parallel composition of two or more contracts; variables with the same name are connected.
  • conjoin and disjoin: greatest lower bound and least upper bound.
  • merge: merging of viewpoints of the same component.
  • quotient: the contract of a missing component.
  • separation: the adjoint of merging.
  • saturate: the equivalent contract (A, A -> G).
  • simplify: removes the assumptions and guarantees implied by the other terms of the contract.

Contract operations that check contracts (each tool returns a status and, when the check fails, a counterexample):

  • refines: whether a contract refines another one, and which part fails (assumptions or guarantees).
  • consistent: whether some behavior satisfies the contract.
  • compatible: whether some environment satisfies the assumptions.
  • realizable: GR(1) realizability, with the controller or the counter-strategy of the environment.
  • contains_environment and contains_implementation: whether an environment or an implementation, given as LTL formulas, is accepted by the contract.

LTL formulas:

  • implies and satisfiable: validity of an implication and satisfiability of a formula.

Specifications:

  • read_spec and write_spec: read and write contracts in the logics language of CHASE (the .ltl files of logics_tool).

A contract is given in JSON:

{"name": "Arbiter",
 "inputs": {"req": "bool", "n": [0, 3]},
 "outputs": {"grant": "bool"},
 "assumptions": ["G F !req"],
 "guarantees": ["G (req -> F grant)"]}

Variables are Boolean ("bool") or bounded integers ([low, high]). Formulas use the standard LTL syntax: ! & | -> <-> G F X U R W, relations, arithmetic, and x' for the value of x in the next step.

Requirements

  • The CHASE Python package, chase-cps, installed with the server.
  • A model checker for the checks: nuXmv (preferred) or NuSMV, in the PATH or given with the environment variables CHASE_NUXMV and CHASE_NUSMV.
  • slugs for the realizability checks.

chase-cps install nuxmv --accept-license (or nusmv, slugs) installs them, and chase-cps doctor shows the tools found. The CHASE Docker image contains all of them.

Installation

The server is a Python program that runs on the standard input and output. With uv there is nothing to install: uvx chase-mcp downloads and runs it. Otherwise:

$> pip install chase-mcp

and use chase-mcp in place of uvx chase-mcp below.

Until the first release on PyPI, install from the repositories:

$> pip install "chase-cps @ git+https://github.com/chase-cps/chase" "chase-mcp @ git+https://github.com/chase-cps/chase-mcp"

Claude Code

The server and the skill are packaged as a plugin:

/plugin marketplace add chase-cps/chase-mcp
/plugin install chase@chase-cps

Or only the server:

$> claude mcp add chase -- uvx chase-mcp

Codex

$> codex mcp add chase -- uvx chase-mcp

or, in ~/.codex/config.toml:

[mcp_servers.chase]
command = "uvx"
args = ["chase-mcp"]

Cursor, Claude Desktop, Gemini CLI, and other clients

Most MCP clients read the same JSON configuration. Add the server to the mcpServers object of the configuration file (.cursor/mcp.json or ~/.cursor/mcp.json for Cursor, claude_desktop_config.json for Claude Desktop, ~/.gemini/settings.json for Gemini CLI):

{"mcpServers": {"chase": {"command": "uvx", "args": ["chase-mcp"]}}}

VS Code uses servers in .vscode/mcp.json:

{"servers": {"chase": {"type": "stdio", "command": "uvx", "args": ["chase-mcp"]}}}

OpenCode uses mcp in opencode.json:

{"mcp": {"chase": {"type": "local", "command": ["uvx", "chase-mcp"]}}}

To set the model checker, add "env": {"CHASE_NUXMV": "/path/to/nuXmv"} to the server entry (environment in OpenCode, env = { ... } in Codex).

Skill

The skill is in skills/chase-contracts. It follows the Agent Skills format (a SKILL.md file with instructions), and it also works without the MCP server: the agent then uses the chase_cps Python package. The examples folder of the skill contains complete examples; each one is run by the tests, so the results they state are the ones CHASE returns.

  • Claude Code: installed with the plugin, or copy the folder to ~/.claude/skills/.
  • Codex: copy the folder to ~/.agents/skills/ (or .agents/skills/ in a project).
  • Other agents that support Agent Skills: copy the folder to their skills directory.

Testing

$> pip install ".[test]"

$> pytest

The tests need nuXmv or NuSMV in the PATH.

License

This project is released under the 3-Clause BSD License.

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