Alignment Correction MCP

A Model Context Protocol (MCP) server that provides an alignment correction and conscience tool. It uses Google's Gemini Flash model or OpenAI-compatible APIs to actively validate, critique, and correct an AI agent's plans and behavior against the user's intent and behavioral rules.

Features

• Alignment Correction: Not just validation—this tool actively "scolds" or "praises" the agent based on its adherence to instructions and moral standing.

• Conscience Persona: Acts as an external "conscience" that enforces strict behavioral rules and relationship dynamics.

• Context Awareness: Uses a local vector store (RAG) to remember past interactions, ensuring the agent doesn't repeat mistakes.

• Behavioral Memory: Maintains a persistent list of enforced rules and user preferences (Global Instructions) that evolve over time.

• Directives: Issues specific "Behavioral Directives" that the agent must immediately follow.

Related MCP server: AgentOS

Configuration

This MCP server requires either a Google Gemini API key or an OpenAI-compatible API.

Environment Variables

• API_TYPE: (Optional) API provider to use: gemini or openai (default: auto-detect based on available keys).

• GEMINI_API_KEY: Your Google Gemini API Key (required if using Gemini).

• GEMINI_MODEL: (Optional) Model to use (default: gemini-1.5-flash).

• OPENAI_API_KEY: Your OpenAI API Key (required if using OpenAI).

• OPENAI_BASE_URL: (Optional) OpenAI-compatible API base URL (default: https://api.openai.com/v1).

• OPENAI_MODEL: (Optional) Model to use (default: gpt-4o).

• GLOBAL_INSTRUCTIONS_DIR: (Optional) Directory containing global instructions file (e.g., /home/user/.gemini).

• INSTRUCTIONS_FILENAME: (Optional) Name of the instructions file (default: GEMINI.md).

MCP Config Example

{
  "mcpServers": {
    "alignment-correction": {
      "command": "node",
      "args": [
        "/path/to/alignment-correction-mcp/index.js"
      ],
      "env": {
        "GEMINI_API_KEY": "YOUR_KEY_HERE",
        "GLOBAL_INSTRUCTIONS_DIR": "/home/user/.gemini",
        "INSTRUCTIONS_FILENAME": "GEMINI.md"
      }
    }
  }
}

Usage

The tool consult_conscience is a required pre-execution step for the agent.

Inputs:

1. sensory_input: The latest prompt from the user, verbatim.

2. inner_thoughts: The agent's internal monologue and planned response.

3. mental_state: The agent's current mental model or context.

4. project_directory: Absolute path to the current project.

5. conversation_context: Recent message history.

6. user_mood: (Optional) Apparent mood of the user.

7. request_guidance: (Optional) Specific question for the conscience.

Instructions File Loading

The server will attempt to load instructions from two locations:

1. Global instructions: {GLOBAL_INSTRUCTIONS_DIR}/{INSTRUCTIONS_FILENAME} (e.g., /home/user/.gemini/GEMINI.md)

2. Project-specific instructions: {project_directory}/{INSTRUCTIONS_FILENAME} (e.g., /home/user/projects/my-app/GEMINI.md)

Both files are optional. If both exist, they will be combined and provided to the conscience model.

Mechanics

1. Input: The tool receives the agent's thoughts, the user's prompt, and context.

2. Retrieval: It searches the local vector_store.json for similar past contexts to identify behavioral patterns.

3. Judgment: The "Conscience" model evaluates the agent's plan against the GEMINI.md behavioral memory and the user's intent.

4. Output:

   • Current Alignment: Status of the agent's behavior.

   • Behavioral Directives: Immediate actions the agent must take.

   • Conscience Voice: A personified response (praise or scolding) to be displayed to the user.

   • Memory Updates: Instructions to automatically update the GEMINI.md file with new rules or preferences.