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

The tools are in three layers.

1. Contract algebra. The operations that build contracts; each tool returns a new contract. They hold for any assume-guarantee contracts.

• compose: parallel composition of two or more contracts; variables with the same name are connected. With discharge, the result keeps the terms of the contracts and drops the assumptions discharged by the other contracts (this uses the checks of layer 2).
• conjoin and disjoin: conjunction and disjunction (greatest lower bound and least upper bound).
• merge: merging of viewpoints of the same component.
• divide: the quotient, i.e. the contract of a missing component.
• separate: separation, the adjoint of merging.
• imply and coimply: implication and coimplication, the adjoints of conjunction and disjunction.
• invert: the reciprocal (G, A), the contract seen from its environment.
• assume and ensure: add an assumption or a guarantee.
• saturate: the equivalent contract (A, A -> G).
• simplify: rewrites the formulas, as the simplify command of logics_tool (negations moved to the atoms, temporal operators grouped).

2. Checks. Each tool returns a status and, when the check fails, a counterexample. The checks on contracts reduce to queries on formulas, which nuXmv decides. They take a context: formulas that every behavior satisfies, such as the axiom of an encoding of finite traces.

• refines: whether a contract refines another one, and which part fails (assumptions or guarantees).
• verify: compatibility and consistency; is_compatible (some environment satisfies the assumptions) and is_consistent (some behavior satisfies the contract) check them one at a time.
• implements and accepts: whether an implementation satisfies the contract, and whether the contract accepts an environment, both given as LTL formulas.
• remove_redundant: removes the assumptions and guarantees implied by the other terms of the contract.
• implies and is_satisfiable, on formulas: validity of an implication, and satisfiability of a formula (some behavior satisfies it; this is not the satisfaction of a contract by a component, which is implements).
• unsat_core: a minimal set of terms that cannot hold together; is_compatible, is_consistent, and is_satisfiable return it with core.

3. GR(1) synthesis, with slugs. These tools are about synthesizing a component from a contract in the GR(1) fragment of LTL, not about contracts in general.

• is_realizable: GR(1) realizability, with the counter-strategy of the environment when unrealizable; synthesize also builds the controller.
• unrealizable_core: a minimal set of guarantees that is unrealizable; is_realizable returns it with core.
• is_well_separated: whether the component can win by forcing the environment to violate its assumptions, with that strategy and the assumptions that cause it.
• remove_vacuous: removes the terms whose removal keeps realizability and the controllers (where remove_redundant keeps A -> G).

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"), bounded integers ([low, high]), or reals ("real"). Formulas use the standard LTL syntax: ! & | -> <-> G F X U R W, relations, arithmetic, numbers such as 20.5, and x' for the value of x in the next step.

Requirements

• The CHASE Python package, chase-cps, installed with the server.
• nuXmv for the checks (free for academic and non-commercial use), in the PATH or given with the environment variable CHASE_NUXMV; NuSMV where nuXmv cannot be used.
• slugs for the realizability checks.

chase-cps install nuxmv --accept-license and chase-cps install slugs install 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 (chase-cps install nuxmv --accept-license).

License

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