FlowCheck MCP Server

FlowCheck MCP Server

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🛡️ Production-Grade Safety Layer for AI-First Development

_Git hygiene monitoring + Security scanning + Semantic search + Full observability_

  

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Why FlowCheck?

AI coding assistants are incredibly productive—but they can also create massive, hard-to-review changesets and security risks. FlowCheck is a production-grade safety layer that:

• 🔍 Monitors Git state in real-time during AI-assisted coding
• 🔒 Scans for security issues (PII, secrets, prompt injection attacks)
• 🔎 Semantic history search - find commits by meaning, not keywords
• 📊 Tracks flow health (time, lines, branch age, drift from main)
• 📝 Full observability (OpenTelemetry traces, audit logs)
• 🎯 Intent validation (ticket-to-diff alignment)
• 🤖 Designed for AI agents with enforceable rules

Think of FlowCheck as a "smart fitness watch with a biometric lock" for your codebase—it helps you code faster while actively defending against security threats.

AI-First Design

FlowCheck is built specifically for the agentic coding workflow:

flowchart LR
    Agent["🤖 AI Agent<br/>(Claude, Cursor, etc)"]
    FC["🛡️ FlowCheck<br/>MCP Server"]
    Git["📁 Git Repo<br/>(.git)"]

    Agent -->|"get_flow_state()"| FC
    FC -->|"analyze"| Git
    Git -->|"metrics"| FC
    FC -->|"status: warning<br/>security_flags: [...]"| Agent

    Agent -->|"⏸️ Pause & suggest<br/>checkpoint commit"| Agent

Agent Rules (Recommended)

Copy rules/flowcheck-rules.md to your AI tool's rules directory:

# For Cursor
cp rules/flowcheck-rules.md .cursor/rules/

# For Claude Projects
cp rules/flowcheck-rules.md .claude/rules/

# For other tools
cp rules/flowcheck-rules.md .agent/rules/

This instructs AI agents to automatically check Git hygiene before starting tasks and to pause when thresholds are exceeded.

Quick Start

Installation

git clone https://github.com/backslash-ux/flowcheck.git
cd flowcheck

python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Claude Desktop Integration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "flowcheck": {
      "command": "/path/to/flowcheck/.venv/bin/python",
      "args": ["-m", "flowcheck.server"],
      "env": {
        "PYTHONPATH": "/path/to/flowcheck/src"
      }
    }
  }
}

MCP Tools

Core Tools

| Tool | Purpose | | --------------------- | ---------------------------------------------------- | | get_flow_state | Returns metrics + security_flags (PII/injection) | | get_recommendations | Returns actionable nudges + security warnings | | set_rules | Dynamically adjust thresholds |

v0.2 Smart Features (New)

| Tool | Purpose | | ------------------ | ------------------------------------------------------------------------------------- | | search_history | Semantic search - find commits by meaning | | verify_intent | Smart Intent Verification - use "AI Judge" (BYOK LLM) to align diffs with tickets | | sanitize_content | PII/secret redaction before sharing with AI |

Example: verify_intent (Smart Mode)

{
  "alignment_score": 0.4,
  "is_aligned": false,
  "ticket_id": "42",
  "scope_creep_warnings": ["Scope Creep Detected by AI Judge"],
  "reasoning": "The ticket asks for a bug fix in auth, but the diff contains a full refactor of the billing module."
}

Configuration

FlowCheck supports hierarchical configuration:

1. Repo Config: .flowcheck.json (in project root)
2. Global Config: ~/.flowcheck/config.json
3. Defaults

.flowcheck.json Example

{
  "max_minutes_without_commit": 45,
  "intent": {
    "provider": "openai",
    "model": "gpt-4o",
    "api_key_env": "OPENAI_API_KEY"
  }
}

Ignoring Files (.flowcheckignore)

Create a .flowcheckignore file in your repo root to exclude files from analysis (uses gitignore syntax):

tests/fixtures/
*.min.js
legacy/

Security Features

Guardian Layer

• PII Detection: Emails, phone numbers, SSNs, credit cards
• Secret Scanning: AWS keys, GitHub tokens, API keys, passwords
• Injection Filtering: Detects prompt injection attacks in diffs

Observability

• OpenTelemetry Traces: gen_ai.* semantic conventions
• Audit Logs: Append-only JSON Lines format in ~/.flowcheck/audit.log
• All tool invocations are logged with trace IDs

Semantic Search

• TF-IDF Vectorization: No external ML dependencies
• SQLite Storage: Local index in ~/.flowcheck/semantic_index.db
• Find commits by meaning, not just keywords

Installation & Deployment

Option 1: Docker (Recommended)

Get FlowCheck running in 30 seconds:

# Clone and setup
git clone https://github.com/backslash-ux/flowcheck.git
cd flowcheck

# Configure environment
cp .env.example .env
nano .env  # Add your API keys

# Start the stack
docker-compose up

FlowCheck is now running at http://localhost:8000

See Docker Deployment Guide for:

• Local development setup
• Production deployment
• Image variants (production/slim/dev)
• Troubleshooting

Option 2: Python Package

pip install git+https://github.com/backslash-ux/flowcheck.git

# Set environment variables
export ANTHROPIC_API_KEY=sk-ant-xxxxx

# Start server
flowcheck-server

Option 3: From Source

git clone https://github.com/backslash-ux/flowcheck.git
cd flowcheck

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest tests/

# Start server
flowcheck-server

Deployment

FlowCheck supports multiple deployment models:

| Scenario | Time | Guide | |----------|------|-------| | Local Development | 5 min | Docker | | Production (Docker) | 10 min | Docker.md | | Kubernetes | 30 min | Kubernetes.md | | CI/CD Integration | 15 min | CI-CD.md |

👉 Deployment Guide for full options

Development

# Setup development environment
docker-compose -f docker-compose.dev.yml up

# Or use Python venv
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src/flowcheck

Philosophy

FlowCheck embodies the principle that good Git hygiene enables good AI collaboration:

1. Smaller commits are easier for humans to review and audit
2. Frequent checkpoints prevent losing work during long sessions
3. Clean history makes it easier to understand what the AI changed
4. Non-blocking nudges preserve developer autonomy

License

MIT