DALI2-Agent-Brain-MCP

MCP server that exposes the DALI2-Agent-Brain logic reasoner as a set of tools for any MCP-compatible client (Unsloth Studio, Claude Desktop, Cursor, etc.).

What it does

The DALI2-Agent-Brain is a symbolic verification system: an LLM proposes an answer, then a Prolog meta-interpreter formally verifies it. This MCP server lets external chat clients call that verification pipeline as a tool.

Unsloth Studio chat  ──MCP──►  dali2-mcp-server  ──REST──►  DALI2-Agent-Brain
 (LLM generates                  (this repo)                 (Prolog verifier)
  a response)                                                  │
                                                               ▼
                                                    verified / unverified
                                                    + derivation trace

Tools exposed

Prerequisites

1. DALI2-Agent-Brain running — start it from the main repo:

   cd DALI2-Agent-Brain
   start.bat sk-or-YOUR_OPENROUTER_KEY

   This brings up Redis + DALI2 + the GUI on http://localhost:8090.

2. Python 3.10+ with the MCP dependencies:

   pip install -r requirements.txt

   Or with uv:

   uv sync

3. Copy .env.example to .env and adjust if needed:

   cp .env.example .env

Configuration for Unsloth Studio

Use mcp_config.json (or mcp_config_uv.json if using uv):

{
  "mcpServers": {
    "dali2-logic-reasoner": {
      "command": "python",
      "args": ["-m", "dali2_mcp.server"],
      "env": {
        "DALI2_URL": "http://localhost:8090"
      }
    }
  }
}

Paste this into the Unsloth Studio MCP tools configuration panel. Adjust DALI2_URL if the DALI2-Agent-Brain runs on a different host/port.

Using uv (recommended)

If you use uv as your Python package manager, use mcp_config_uv.json instead:

{
  "mcpServers": {
    "dali2-logic-reasoner": {
      "command": "uv",
      "args": ["run", "dali2-mcp"],
      "env": {
        "DALI2_URL": "http://localhost:8090"
      }
    }
  }
}

Running the server manually (for testing)

python -m dali2_mcp.server

The server communicates over stdio (standard MCP transport). It will wait for JSON-RPC messages on stdin and respond on stdout.

How it works

1. The MCP client (e.g. Unsloth Studio chat) calls a tool like solve_and_verify with a reasoning question.

2. This server forwards it as POST /api/ask to the DALI2-Agent-Brain GUI.

3. The DALI2 brain:

   • Classifies the task (syllogism, propositional, argument, math)

   • Asks the LLM to propose a structured answer

   • Runs the symbolic verifier (finite model checker / truth-table checker / arithmetic verifier)

   • Returns: answer, verified (true/false), and a derivation trace

4. This server formats the result and returns it to the chat client.

The key property: a wrong-but-confident LLM answer is never accepted, because acceptance is gated by symbolic verification.

Project structure

DALI2-Agent-Brain-MCP/
├── src/dali2_mcp/
│   ├── __init__.py
│   └── server.py          MCP server (stdio transport)
├── mcp_config.json        Config for Unsloth Studio (pip install)
├── mcp_config_uv.json     Config for Unsloth Studio (uv)
├── requirements.txt       Python dependencies
├── pyproject.toml         Package metadata + entry point
├── .env.example           Environment variable template
└── README.md

Environment variables