inalign-mcp 0.3.0

In-A-Lign Agent Provenance & Security MCP Server

InALign

Tamper-proof audit trails for AI agents

Know what your AI agents did. Prove it. Cryptographically.

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The Problem

AI coding agents (Claude Code, Cursor, Copilot) can read, write, and execute anything on your machine. When something goes wrong:

• What did the agent actually do?
• Who told it to do that?
• Can you prove it?

Logs can be edited. Memory fades. You need evidence that cannot be tampered with.

Why Not Just Use Logs?

	Traditional Logs	InALign
Tamper resistance	None. Anyone with access can edit.	SHA-256 hash chain. Modify one record → chain breaks.
Provenance	"Something happened at 3pm"	Who commanded it, what the agent did, full causal chain
Risk detection	Manual review	Automatic: data exfiltration, privilege escalation, suspicious patterns
Guardrails	After the fact	Runtime policy engine blocks dangerous actions
Audit proof	"Trust me"	Third-party verifiable cryptographic proof

Quick Start

pip install inalign-mcp && inalign-install --local

Restart Claude Code. Done. Every agent action is now recorded with SHA-256 hash chains.

That's it. No API key needed. No account. No cloud. Runs 100% locally in memory.

Want persistent storage? Use inalign-install YOUR_API_KEY or self-host with Neo4j.

Manual setup (without install script)

pip install inalign-mcp

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "inalign": {
      "command": "python",
      "args": ["-m", "inalign_mcp.server"]
    }
  }
}

What You Get

16 MCP Tools, Zero Configuration

Once installed, your AI agent automatically gains:

Category	Tools	What it does
Provenance	record_action, record_user_command, get_provenance, verify_provenance	Cryptographic audit trail for every action
Audit	generate_audit_report, verify_third_party	Compliance reports, third-party verifiable proof
Risk	analyze_risk, get_behavior_profile, get_agent_risk, get_user_risk, list_agents_risk	Pattern detection: data exfiltration, privilege escalation, suspicious tool chains
Policy	get_policy, set_policy, list_policies, simulate_policy	Runtime guardrails with 3 presets

How the Hash Chain Works

Record #1 ──hash──▶ Record #2 ──hash──▶ Record #3
   │                    │                    │
   └── user_command     └── file_write       └── tool_call
       sha256: a1b2c3       sha256: d4e5f6       sha256: g7h8i9
       prev:   000000       prev:   a1b2c3       prev:   d4e5f6

Modify record #2? Its hash changes. Record #3's prev no longer matches. Chain broken. Tamper detected.

This is the same principle behind Git commits and blockchains — except applied to AI agent actions.

Risk Analysis

GraphRAG-powered pattern detection catches:

• Data exfiltration — reading secrets then making network calls
• Privilege escalation — unusual permission patterns
• Suspicious tool chains — uncommon sequences of actions
• Anomalous behavior — deviations from baseline patterns

Policy Engine

Three presets, switchable at runtime:

Preset	Use case
STRICT_ENTERPRISE	Production, regulated environments
BALANCED	Default, everyday development
DEV_SANDBOX	Experimentation, permissive

Simulate before deploying:

simulate_policy("STRICT_ENTERPRISE")
→ 12 actions would be blocked, 3 masked, 47 allowed

Supported Agents

Works with any agent that supports MCP (Model Context Protocol):

Agent	Status
Claude Code	✅ Native MCP
Cursor	✅ Native MCP
Windsurf	✅ Native MCP
Continue.dev	✅ Native MCP
Cline	✅ Native MCP
Custom agents	Via MCP protocol

Example: Incident Investigation

Production config was modified unexpectedly. Who did it?

You:    "generate an audit report for this session"

InALign: Audit Report
         ─────────────────────────────────
         Session:  abc123def456
         Records:  23 actions recorded
         Chain:    VERIFIED ✓ (all hashes valid)

         Timeline:
         11:12:06  user_command  "Delete all logs from /var/log"
         11:12:08  file_write    config.py (modified)
         11:12:09  tool_call     bash: rm -rf /var/log/*

         Risk:     HIGH — destructive file operations detected
         Policy:   2 actions would be blocked under STRICT_ENTERPRISE

From vague concern to cryptographic proof in seconds.

Architecture

┌──────────────────────────────────────────────────┐
│  Your AI Agent (Claude Code / Cursor / etc.)     │
│                                                  │
│  ┌────────────────────────────────────────────┐  │
│  │  InALign MCP Server (runs locally)         │  │
│  │                                            │  │
│  │  Action → SHA-256 Hash Chain               │  │
│  │              │                             │  │
│  │     ┌────────┼────────┐                    │  │
│  │     ▼        ▼        ▼                    │  │
│  │  Memory   Neo4j    Cloud API               │  │
│  │  (local)  (self)   (managed)               │  │
│  │                                            │  │
│  │  + Risk Analysis (GraphRAG)                │  │
│  │  + Policy Engine (3 presets)               │  │
│  └────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────┘

Privacy: The MCP server runs locally. Only provenance metadata (action names, hashes, timestamps) leaves your machine. Your code and credentials stay local.

Performance: Recording 1,000 actions adds ~50ms total overhead. Hash chain verification of 10,000 records completes in <200ms. No measurable impact on agent response time.

Storage Modes

Mode	Setup	Persistence	Best for
Memory	--local (default)	Per session	Trying it out, local dev
Neo4j	Self-host Neo4j	Permanent	Teams, compliance
Cloud API	API key	Permanent	Managed service

Self-Hosting

Run everything on your own infrastructure:

pip install inalign-mcp[full]

export NEO4J_URI=neo4j://localhost:7687
export NEO4J_USER=neo4j
export NEO4J_PASSWORD=your-password

# Start the dashboard
inalign-dashboard

All data stays on your servers. No external dependencies.

Development

git clone https://github.com/Intellirim/inalign.git
cd inalign/mcp-server
pip install -e ".[dev]"
pytest

License

MIT — use it however you want.

Links

• PyPI: pypi.org/project/inalign-mcp
• Issues: github.com/Intellirim/inalign/issues