AIR Blackbox MCP Server

EU AI Act compliance scanning for Claude Desktop, Claude Code, Cursor, and any MCP-compatible client.

Unlike other compliance scanners that only report problems, AIR Blackbox also remediates - generating working code fixes, trust layer integrations, GDPR compliance checks, bias analysis, and full compliance reports. Under the hood, the scanning feeds into air-trust, a cryptographic audit chain (HMAC-SHA256) with Ed25519 signed handoffs that ensures compliance data integrity.

14 Tools (10 base + 4 SDK-powered)

Supported Frameworks

LangChain, CrewAI, AutoGen, OpenAI, Haystack, LlamaIndex, Semantic Kernel, Google ADK, Claude Agent SDK, and generic RAG pipelines.

Installation

Basic (10 tools, no SDK features)

pip install air-blackbox-mcp

Works standalone with just the lightweight built-in scanner.

Full (14 tools with GDPR, bias, validation, and history)

pip install air-blackbox-mcp[full]

Installs the full `air-blackbox` SDK (`>=1.13,=1.0`, no upper bound.**

mcp 2.0 removed `mcp.server.fastmcp` and replaced `FastMCP` with `MCPServer`.

Rather than pin away from it, the server detects which generation is installed

and binds to the right class, so it runs on 1.x and 2.x alike:

Both paths are covered by tests that launch `python -m air_blackbox_mcp` as a

real subprocess and drive it over stdio — the same way Claude Desktop and

Cursor do — and the full suite runs green on both.

If you are on 0.2.3, upgrade. That version declared an unpinned

`mcp>=1.0.0`, so once mcp 2.0 shipped, every fresh install produced a server

that died on import:

ModuleNotFoundError: No module named 'mcp.server.fastmcp'

pip install --upgrade air-blackbox-mcp

0.2.4 fixed it by capping at `mcp<2`; 0.3.0 removes the cap entirely, so this

server no longer conflicts with anything built for mcp 2.x sharing the same

environment.

Claude Desktop Setup

Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:

{
  "mcpServers": {
    "air-blackbox": {
      "command": "python3",
      "args": ["-m", "air_blackbox_mcp"]
    }
  }
}

Restart Claude Desktop. The 14 tools will appear automatically.

Claude Code / Cursor Setup

Add to `.cursor/mcp.json` in your project:

{
  "mcpServers": {
    "air-blackbox": {
      "command": "python3",
      "args": ["-m", "air_blackbox_mcp"]
    }
  }
}

Or add to `.claude/mcp.json` for Claude Code.

Usage Examples

In Claude Desktop, Claude Code, or Cursor, just ask:

• "Scan this code for EU AI Act compliance"
• "Add a trust layer to this LangChain agent"
• "Check this text for prompt injection"
• "What does Article 12 require?"
• "Generate a compliance report for ~/myproject"
• "Classify the risk level of `send_email`"
• "Scan this code for GDPR issues" (requires full SDK)
• "Check for bias in this AI model code" (requires full SDK)
• "Can my agent call this shell function?" (requires full SDK)
• "Show me my compliance trends" (requires full SDK)

SDK Features (Optional)

The full `air-blackbox` SDK unlocks 4 additional tools:

1. GDPR Scanning (`scan_gdpr`)

2. Bias Analysis (`scan_bias`)

3. Action Validation (`validate_action`)

4. Compliance History (`compliance_history`)

Optional: Deep Analysis with Ollama

For AI-powered analysis beyond regex patterns:

# Install Ollama
brew install ollama

# Pull the fine-tuned compliance model
ollama pull air-compliance-v2

# The analyze_with_model tool will automatically use it

What Makes This Different

Other MCP compliance tools only scan. AIR Blackbox:

1. Scans + Remediates - finds issues across 6 EU AI Act articles AND generates working code fixes

2. Analyzes deeply - regex patterns + AI-powered model analysis + prompt injection detection (15 patterns)

3. Validates before execution - pre-approval gates and risk classification for agent actions (Article 14)

4. Tracks compliance - GDPR checks, bias analysis, full reports, and historical trend monitoring (SDK)

Architecture

Which engine runs is fixed per tool, not a runtime fallback. Earlier versions

of this README described a "try the SDK first, fall back to built-in" pattern.

That was never what the code did, and it mattered: a reader could not tell

whether two reports came from the same rules. The actual behavior:

So a given tool produces results from the same engine on every install, and

there is no silent switch between engines.

Result provenance

Because a compliance finding is only comparable to another if you know what

produced it, every machine-readable result carries a `provenance` block:

{
  "findings": [ ... ],
  "provenance": {
    "engine": "builtin-rules",
    "scanner_version": "0.2.4",
    "ruleset_id": "eu-ai-act-art9-15",
    "ruleset_version": "cee71577c486",
    "sdk_version": null
  }
}

• `engine` — `builtin-rules` or `air-blackbox-sdk`, whichever actually ran.
• `ruleset_version` — a content hash of the active rules, not a hand-maintained

string. Change a regex and it changes by itself; a version someone must

remember to bump is one that eventually misreports which rules ran.

• `sdk_version` — the SDK that *produced* this result, so it is `null` for

built-in results even when the SDK is installed alongside. Reporting a

version that contributed nothing would imply its rules ran.

Two reports with the same `engine` + `ruleset_version` were produced by

byte-identical rules and can be diffed directly. Different values mean the

rules moved, and the diff needs that context to be meaningful.

Errors carry provenance too — knowing which version produced an error is as

useful as knowing which version produced a finding.

Install `[full]` to unlock the Tier 5 SDK tools; the base install works

standalone.

Part of AIR Blackbox

This MCP server is part of the AIR Blackbox ecosystem:

• air-trust on PyPI - the cryptographic audit chain that backs compliance scanning
• air-blackbox on PyPI - the full compliance SDK and CLI scanner
• **airblackbox.ai** - the project homepage and docs

Links

• EU AI Act - the regulation
• GDPR - data protection regulation