🛡️ Blind Auditor - MCP Server

"Code drunk, audit sober." — The Philosophy of Blind Auditor

Blind Auditor is a mandatory code auditing system built on the MCP (Model Context Protocol). It uses a unique "Thinking Isolation" mechanism to force AI Agents to enter an independent "audit phase" and self-review their code before outputting the final result.

🧠 Core Philosophy: Thinking Isolation

Traditional AI coding is often "generate and output," which allows errors and biases to slip through. Blind Auditor introduces a middle layer:

1. Intercept: When the Agent wants to output code, it must first submit it to Blind Auditor.
2. Isolate: Blind Auditor does not return the result immediately. Instead, it injects a mandatory system instruction, forcing the Agent to pause its current persona and switch to a "Ruthless Auditor" role.
3. Audit: In this isolated context, the Agent must scan the generated code line by line against the predefined rules.json.
4. Release: The code is unlocked and returned to the user only when the audit score meets the threshold (default > 80) and there are no Critical issues.

🎯 Key Features

• 🛡️ Zero Trust Architecture: Default distrust of the Agent's initial draft; it must pass an audit.
• 💰 Zero Extra Cost: Reuses the host IDE's current session model, requiring no additional API Key.
• ⚖️ Bias Removal: Forces a perspective switch via Prompt injection to break generation inertia.
• 📏 Strict Compliance: Hard-codes team code standards (rules.json) into the generation process, which is more effective than simple Prompts.
• 🔄 Auto-Fix Loop: Automatically triggers a "fix-resubmit" loop upon audit failure until compliance is met or the maximum retry count is reached.

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🚀 Quick Start

1. Prerequisites

This project uses uv for dependency management (pip is also supported).

# Enter project directory
cd blind-auditor

# Install dependencies
uv sync
# Or
pip install -r requirements.txt

2. Configure Audit Rules (rules.json)

Create or edit rules.json in the project root. This is the auditor's "Rulebook".

{
  "project_name": "MyCoreProject",
  "strict_mode": true,
  "max_retries": 3,
  "rules": [
    {
      "id": "SEC-001",
      "severity": "CRITICAL",
      "description": "Strictly prohibit hardcoding any API Keys, passwords, or sensitive Tokens.",
      "weight": 50
    },
    {
      "id": "PERF-001",
      "severity": "WARNING",
      "description": "Avoid database queries or IO operations inside loops.",
      "weight": 15
    },
    {
      "id": "STYLE-001",
      "severity": "PREFERENCE",
      "description": "Use snake_case for variable names.",
      "weight": 5
    }
  ]
}

Configuration Fields:

• severity:
  • CRITICAL: Fatal error. If found, the audit fails immediately. Usually carries a high penalty.
  • WARNING: Warning. Moderate penalty; accumulation can lead to failure.
  • PREFERENCE: Style preference. Low penalty.
• weight: The penalty points deducted when this rule is violated (Total score: 100).

3. Configure IDE (Antigravity / Cursor / Windsurf)

Step A: Add System Prompt (Critical)

To ensure the Agent knows it must use this tool, add the following instruction to your .cursorrules or IDE System Prompt settings:

# Blind Auditor Protocol
**CRITICAL INSTRUCTION**:
You are integrated with the **Blind Auditor MCP**.
Whenever you generate code for a user request, you **MUST NOT** output the code block directly to the user.

**Execution Protocol**:
1.  **Draft**: Generate the code internally in your thought process.
2.  **Submit**: Call the `submit_draft` tool with your code and language.
3.  **Obey**: The tool will return a strict "Audit Instruction". You MUST follow it exactly to audit your own code.
4.  **Finalize**: Only when the tool returns "✅ AUDIT PASSED" can you display the final code to the user.

**Prohibited**:
- Do NOT output code blocks before passing the audit.
- Do NOT skip the audit step for "simple" changes.

Step B: Configure MCP Server

Add the following to your MCP client configuration file (e.g., claude_desktop_config.json or IDE settings):

{
  "mcpServers": {
    "blind-auditor": {
      "command": "python",
      "args": ["-m", "src.main"],
      "cwd": "/absolute/path/to/blind-auditor"
    }
  }
}

---

🔧 Tool Details

1. submit_draft

Submit a code draft.

• Input: code (content), language (programming language)
• Behavior: Locks the session and returns mandatory audit instructions.

2. submit_audit_result

Submit your audit conclusion.

• Input:
  • passed (bool): Whether you believe it passed.
  • issues (list): List of issues found.
  • score (int): Score from 0-100.
• Behavior:
  • If score < 80, forces passed=False.
  • If passed, unlocks the code.
  • If failed, increments retry count and requires the Agent to fix and resubmit.

3. reset_session

Resets the state and clears the retry count.

---

🔁 Workflow Diagram

graph TD
    User[User Request] --> Agent
    Agent[Agent Generates Draft] -->|1. submit_draft| MCP
    MCP -->|2. Inject Audit Instructions| Agent
    
    subgraph "Thinking Isolation"
        Agent -->|3. Self-Review| Agent
        Agent -->|4. submit_audit_result| MCP
    end
    
    MCP -->|5. Verdict| Decision{Passed?}
    
    Decision -->|No (Issues Found)| Retry[Retry Count +1]
    Retry -->|Limit Not Reached| Fix[Agent Fixes Code]
    Fix -->|Resubmit| Agent
    
    Decision -->|Yes (Score >= 80)| Final[✅ Output Final Code]
    
    Retry -->|Limit Reached| Force[⚠️ Force Output (With Warning)]

❓ Troubleshooting

Q: The Agent always outputs code directly without calling tools. A: Check if the System Prompt is configured correctly. You must explicitly tell the Agent "Do NOT output code directly". You can also manually remind it in the chat: "Please audit via Blind Auditor first".

Q: Why does it fail even if I give the code 100 points? A: Check if any CRITICAL rules in rules.json were triggered. Current logic mainly relies on the score passed by the Agent, but if passed is True while score < 80, the system will force a rejection.

Q: Which programming languages are supported? A: Theoretically, all languages are supported. Blind Auditor itself does not parse code syntax but relies on the Agent's understanding to match descriptions in rules.json.

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🛠️ Development Guide

# Run server
python -m src.main

# Debug mode (output to stderr)
# View print statements in src/main.py

📄 License

MIT License