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khirazo

GCM MCP Relay

by khirazo

GCM MCP Relay

⚠️ DEPRECATED — GCM 2.0.2+ Users

GCM 2.0.2 introduced API key authentication for the built-in MCP server, which allows AI coding agents to connect directly without this relay. If you are running GCM 2.0.2 or later, use the direct connection method described in the gcm-api-samples MCP Bob Setup Guide instead.

This relay remains useful only for GCM 2.0.1 (and earlier) deployments where API key authentication is not available.


A secure, Docker-based relay service that sits between AI coding agents and IBM Guardium Cryptography Manager's built-in MCP server, providing simplified authentication and audit logging.

Overview

IBM Guardium Cryptography Manager (GCM) 2.0.1 includes a built-in MCP server with tools for managing cryptographic assets, certificates, and policies. However, direct access from AI agents is challenging due to:

  • Complex OAuth2/OIDC authentication flow

GCM MCP Relay solves these problems by:

  • Docker-first deployment: Containerized for consistent, portable deployment

  • Transparent authentication: Handles OAuth2/OIDC flow automatically

  • stdio transport mode: Local AI agent integration

  • Pass-through architecture: All GCM tools exposed (access controlled by GCM)

Related MCP server: guardrails-mcp-server

Architecture

graph TB
    subgraph docker_host["PC / Laptop (Docker Host)"]
        agent["AI Coding Agent<br/>(IBM Bob / Cursor)"]
        
        subgraph container["Docker Container: GCM MCP Relay"]
            relay["- Authentication management<br/>- Audit logging<br/>- Tool pass-through"]
        end
        
        agent -->|stdio| relay
    end
    
    relay -->|"HTTPS + Bearer JWT"| gcm["GCM Built-in MCP Server<br/>(streamable-http, 26 tools, RBAC enforced)"]
    
    style docker_host fill:#f9f9f9,stroke:#333,stroke-width:2px
    style container fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    style agent fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    style relay fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
    style gcm fill:#fce4ec,stroke:#c2185b,stroke-width:2px

Features

  • Docker deployment: Multi-stage build, non-root user, minimal image

  • stdio mode: Local development with AI coding agents

  • Authentication: Automatic OAuth2/OIDC token management

  • Tool Pass-through: All GCM tools exposed (no filtering)

  • Configuration: TOML config + environment variables

🚀 Quick Start

📖 For detailed setup instructions, see QUICKSTART.md

Prerequisites

Note: This relay is intended for GCM 2.0.1 only. For GCM 2.0.2+, see the deprecation notice above.

  • Docker Desktop (or Docker Engine + Docker Compose)

  • IBM Bob or other MCP client

  • GCM 2.0.1 server with OAuth2/OIDC credentials (username, password, client secret)

  • Git (for cloning the repository)

Setup (3 Steps)

  1. Clone and configure: Copy .env.example to .env and add your GCM credentials

  2. Build container: docker compose build

  3. Configure IBM Bob: Add relay to mcp_settings.json

See QUICKSTART.md for detailed instructions.

Testing

Test the relay locally before connecting to IBM Bob:

# Make test script executable (Linux/macOS/WSL)
chmod +x scripts/test-mcp.sh

# Run MCP protocol test
./scripts/test-mcp.sh

Expected output:

=== GCM MCP Relay Test Script ===

Test 1: Initialize
{
  "protocolVersion": "2024-11-05",
  "serverInfo": {
    "name": "gcm-mcp-relay",
    "version": "0.1.0"
  },
  "capabilities": {
    "tools": {}
  }
}

Test 2: List Tools
Found 32 tools

{
  "name": "search_policies",
  "description": "Retrieve policies filtered by policy_type..."
}
{
  "name": "fetch_policy_by_id",
  "description": "Retrieve one or more policies by their unique policy IDs..."
}
... (showing first 10 tools)

Summary:
- Initialize: ✓ Success
- Tools List: ✓ Success (32 tools)

Verification

# Test relay startup
docker compose run --rm gcm-mcp-relay

# Check logs
docker compose logs gcm-mcp-relay

# Verify configuration
docker compose config

Alternative: Native Python Installation

If you prefer to run without Docker:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export GCM_USERNAME="your-username"
export GCM_PASSWORD="your-password"
export GCM_CLIENT_SECRET="your-client-secret"

# Run relay
python -m src --mode stdio

# With custom config
python -m gcm_relay --mode stdio --config config/relay.toml

MCP Client Configuration

Cursor / Claude Desktop:

{
  "mcpServers": {
    "gcm": {
      "command": "python",
      "args": ["-m", "gcm_relay", "--mode", "stdio"],
      "env": {
        "GCM_USERNAME": "your-username",
        "GCM_PASSWORD": "your-password",
        "GCM_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

📚 Documentation

Getting Started

Architecture & Design

Configuration

Relay Configuration (config/relay.toml)

[relay]
mode = "stdio"
log_level = "WARNING"  # DEBUG, INFO, WARNING, ERROR, CRITICAL
                       # WARNING recommended for production

[gcm]
url = "https://gcm.example.com:31443/ibm/mcp/mcp"
verify_ssl = false

[gcm.auth]
username = ""  # Set via GCM_USERNAME
password = ""  # Set via GCM_PASSWORD
client_id = "gcmclient"
client_secret = ""  # Set via GCM_CLIENT_SECRET

[gcm.oidc]
host = "gcm.example.com"
port = 30443
realm = "gcmrealm"

[audit]
enabled = true
log_file = "logs/audit.jsonl"

Log Levels

The relay supports five log levels:

  • DEBUG: Verbose logging including all MCP protocol details (for troubleshooting)

  • INFO: Normal operation logs (default for development)

  • WARNING: Errors and warnings only (recommended for production)

  • ERROR: Only error messages

  • CRITICAL: Only critical failures

Production Recommendation: Use WARNING level to reduce log noise. Optional MCP methods like resources/list are logged at DEBUG level and won't appear in WARNING mode.

Viewing Logs:

# Docker logs
docker compose logs gcm-mcp-relay

# Follow logs in real-time
docker compose logs -f gcm-mcp-relay

# Audit logs (tool invocations)
cat logs/audit.jsonl | jq

See QUICKSTART.md for detailed log configuration guide.

Available Tools

All tools from GCM MCP server are exposed. Access control is enforced by GCM's RBAC based on the authenticated user's roles.

Example Tools

  • Policy Management: search_policies, fetch_policy_by_id, create_policy

  • Violations: get_violation_by_id, fetch_policy_violations_ticket, policy_violations_dashboard

  • Assets: fetch_detailed_asset_list_by_it_assets, get_asset_groups, etc.

  • Crypto Objects: fetch_detailed_asset_list_by_crypto_objects, get_vulnerable_crypto_objects_count, etc.

  • Certificates: get_certificate_details, get_vault_details, renew_ca_signed_certificate, renew_self_signed_certificate

  • Users: get_user_details_by_username

Access Control: Tool availability depends on the GCM user's assigned roles. Use dedicated service accounts with appropriate permissions.

Security

Credential Management

DO:

  • ✅ Use environment variables for credentials

  • ✅ Set restrictive file permissions (600) on config files

  • ✅ Add config files to .gitignore

  • ✅ Use separate credentials per environment

  • ✅ Create dedicated GCM service accounts with minimal required permissions

DON'T:

  • ❌ Commit credentials to version control

  • ❌ Log credentials (even in debug mode)

  • ❌ Store credentials in plaintext in shared locations

  • ❌ Reuse credentials across environments

Access Control

  • Access control enforced by GCM's native RBAC

  • Configure user roles in GCM admin console

  • Use dedicated service accounts for AI agents

  • Comprehensive audit logging of all tool invocations

Network Security

  • TLS required for GCM connections

  • Certificate verification (production)

  • Configurable timeouts

  • Connection pooling

Audit Logging

All tool invocations are logged in structured JSON format:

{
  "timestamp": "2026-03-27T08:00:00.123Z",
  "event_type": "tool_invocation",
  "tool_name": "search_policies",
  "user": "gcm-service-account",
  "arguments": {"query": "TLS"},
  "result": {
    "status": "success",
    "duration_ms": 234
  }
}

Logs include:

  • Tool invocations (success/failure)

  • Authentication events

  • System events

Development

Project Structure

gcm-mcp-relay/
├── src/gcm_relay/          # Source code
│   ├── server/             # MCP server (stdio)
│   ├── tools/              # Tool management
│   ├── auth/               # Authentication
│   ├── client/             # GCM MCP client
│   ├── audit/              # Audit logging
│   └── config/             # Configuration
├── scripts/                # Utility scripts
│   └── test-mcp.sh        # MCP protocol test
├── docs/                   # Documentation
├── config/                 # Configuration files
└── logs/                   # Log files

Testing

The project includes an MCP protocol test script to verify relay functionality:

# Make test script executable (Linux/macOS/WSL)
chmod +x scripts/test-mcp.sh

# Run MCP protocol test
./scripts/test-mcp.sh

This tests:

  • MCP protocol initialization (2024-11-05)

  • Tool listing (all tools from GCM)

  • JSON-RPC communication over stdio

See Testing section above for expected output.

Note: Unit tests with pytest are planned for future implementation. Currently, use the MCP protocol test script for validation.

Documentation

Troubleshooting

Authentication Fails

# Check credentials
echo $GCM_USERNAME
echo $GCM_PASSWORD
echo $GCM_CLIENT_SECRET

# Check Keycloak is accessible
curl -k https://gcm.example.com:30443/realms/gcmrealm/.well-known/openid-configuration

# Test relay with MCP protocol
./scripts/test-mcp.sh

Tool Access Denied

If a tool call fails with "access denied":

  • Check GCM user roles in GCM admin console

  • Verify the user has required permissions for the tool

  • Review GCM audit logs for permission details

Connection Timeout

# Increase timeout in config
[gcm]
request_timeout = 60

# Check network connectivity
ping gcm.example.com
telnet gcm.example.com 31443

Contributing

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

License

[License information]

References

Support

For issues and questions:


Made with ❤️ for secure AI integration with IBM Guardium Cryptography Manager

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