mcp-forge/docs/architecture1.md

# MCP-Forge Architecture Document

## Overview

MCP-Forge is an MCP (Model Context Protocol) server that provides intelligent code execution capabilities for AI agents. It enables agents to execute Python code in isolated containers while having access to other MCP tools, optimizing data processing by keeping large datasets in the execution environment rather than passing them through the LLM context.

## Core Concept

**Problem:** When AI agents use MCP tools that return large amounts of data, the data must flow through the LLM context window, which is:
- Token-expensive
- Slow
- Limited by context window size

**Solution:** MCP-Forge provides a code execution environment where:
- MCP tools are available as Python functions
- Agents can write code to process data locally
- Only relevant results flow back to the agent
- The agent already knows tool signatures from its own configuration

## Architecture

```
AI Agent (e.g., GitHub Copilot, Claude)
  │
  ├─ MCP Protocol
  │
MCP-Forge Server
  │
  ├─ MCP Resources (Discovery)
  │   ├─ tools/available
  │   ├─ sessions/{id}/state
  │   ├─ sessions/{id}/variables
  │   └─ environments/list
  │
  ├─ MCP Tools (Execution)
  │   ├─ execute_python()
  │   ├─ document_state()
  │   └─ build_custom_environment()
  │
  ├─ Execution Backends
  │   ├─ Simple Backend (stateless)
  │   └─ Jupyter Backend (stateful)
  │
  ├─ Custom Environment Builder
  │   ├─ UV Package Installer
  │   ├─ Build Cache Manager
  │   ├─ Security Validator
  │   └─ Template Library
  │
  └─ Podman API (restricted)
      │
      └─ Isolated Execution Containers
          ├─ Python Runtime
          ├─ MCP Client (injected tools)
          └─ Session Volumes
```

## Components

### 1. MCP Server Interface

#### Resources

**`mcp://forge/tools/available`**
- Returns: List of MCP tool names available in execution environment
- Purpose: Agent discovers what tools it can use in generated code
- Example: `["github_search_repos", "filesystem_read", "sqlite_query"]`

**`mcp://forge/tools/{tool_name}/schema`** (optional)
- Returns: Full JSON schema for a specific tool
- Purpose: Fallback if agent doesn't have tool definition
- Note: Typically not needed as agent has tools in its own config

**`mcp://forge/sessions/{session_id}/state`**
- Returns: Documented state for a session
- Structure:
  ```json
  {
    "documented_variables": {
      "df": "Customer data, 1000 rows, columns: id, name, purchase_date, amount",
      "model": "Trained RandomForest, accuracy 0.87"
    },
    "note": "Preprocessing complete, ready for analysis",
    "last_updated": "2026-02-06T10:30:00Z",
    "all_variables": ["df", "model", "temp", "result"]
  }
  ```

**`mcp://forge/sessions/{session_id}/variables`**
- Returns: List of all variables in session namespace (kernel introspection)
- Purpose: Quick check of what exists in session

**`mcp://forge/environment/info`** (optional)
- Returns: Python version, installed packages, system info
- Purpose: Agent can verify environment capabilities

**`mcp://forge/environments/list`**
- Returns: List of available custom environments and templates
- Structure:
  ```json
  {
    "custom": [
      {
        "name": "my-ml-env",
        "image": "mcp-forge/custom:my-ml-env",
        "description": "Custom ML environment",
        "packages": ["numpy==1.24.0", "pandas==2.0.0", "scikit-learn==1.3.0"],
        "created_at": "2026-02-06T10:00:00Z",
        "size_mb": 1024
      }
    ],
    "templates": [
      {
        "name": "ml-basic",
        "description": "Basic ML stack (numpy, pandas, scikit-learn)",
        "packages": ["numpy", "pandas", "scikit-learn"]
      },
      {
        "name": "data-science",
        "description": "Full data science stack",
        "packages": ["numpy", "pandas", "matplotlib", "seaborn", "jupyter"]
      }
    ]
  }
  ```

**`mcp://forge/audit/operations`** (optional)
- Returns: Audit log of container operations
- Purpose: Security monitoring, debugging

#### Tools

**`execute_python`**

Execute Python code in an isolated container with MCP tools available.

Parameters:
```json
{
  "code": "string (required) - Python code to execute",
  "mcp_tools": "array (optional) - List of MCP tool names to inject",
  "session_id": "string (optional) - Session ID for stateful execution (null = stateless)",
  "backend": "string (optional) - 'simple' (default) or 'jupyter'",
  "timeout": "integer (optional) - Max execution time in seconds (default: 300)",
  "volumes": "object (optional) - Volume mount configuration",
  "custom_image": "string (optional) - Custom environment name to use",
  "environment": "string (optional) - Template environment name (e.g., 'ml-basic', 'data-science')"
}
```

Returns:
```json
{
  "success": true,
  "stdout": "string - Standard output",
  "stderr": "string - Standard error",
  "result": "any - Return value of last expression",
  "available_tools": ["list of tools that were injected"],
  "execution_time": 1.23,
  "session_id": "abc123 (if stateful)"
}
```

Example:
```python
# Agent calls:
execute_python(
  code="""
repos = github_search_repos(query="MCP servers", max_results=100)
high_quality = [r for r in repos['items'] if r['stars'] > 50]
print(f"Found {len(high_quality)} high-quality repos")
high_quality[:10]  # Return top 10
""",
  mcp_tools=["github_search_repos"],
  session_id=None  # stateless
)
```

**`document_state`**

Document important variables in a stateful session for later retrieval.

Parameters:
```json
{
  "session_id": "string (required) - Session to document",
  "variables": "object (required) - Variable name -> description mapping",
  "note": "string (optional) - General note about session state",
  "clear": "boolean (optional) - Clear existing documentation (default: false)"
}
```

Returns:
```json
{
  "success": true,
  "documented_count": 3,
  "session_id": "abc123"
}
```

Example:
```python
document_state(
  session_id="abc123",
  variables={
    "df": "Customer purchase data, 1000 rows, preprocessed and cleaned",
    "model": "Trained RandomForest classifier, 87% accuracy on test set"
  },
  note="Ready for prediction phase"
)
```

**`build_custom_environment`**

Build a custom container image with specified Python packages. This is the **only** way to install additional packages - `pip install` is **not** allowed during code execution for security reasons.

Parameters:
```json
{
  "name": "string (required) - Name for the custom environment (alphanumeric + hyphens)",
  "base_image": "string (optional) - Base image to build from (default: 'python:3.11')",
  "packages": "array (required) - List of Python package specifications",
  "python_version": "string (optional) - Python version: '3.11', '3.12' (default: '3.11')",
  "description": "string (optional) - Description of this environment"
}
```

Returns:
```json
{
  "success": true,
  "image_name": "mcp-forge/custom:my-ml-env",
  "image_id": "sha256:abc123...",
  "build_time": 45.2,
  "installed_packages": ["numpy==1.24.0", "pandas==2.0.0", "scikit-learn==1.3.0"],
  "cache_hit": false
}
```

Example:
```python
# Agent builds custom environment for ML work
build_custom_environment(
  name="my-ml-env",
  packages=[
    "numpy>=1.24.0",
    "pandas>=2.0.0",
    "scikit-learn>=1.3.0",
    "matplotlib>=3.7.0",
    "seaborn>=0.12.0"
  ],
  description="Machine learning environment with common libraries"
)

# Later, use the custom environment
execute_python(
  code="import pandas as pd; df = pd.read_csv('data.csv'); ...",
  custom_image="mcp-forge/custom:my-ml-env",
  session_id="ml-analysis"
)
```

### 2. Execution Backends

#### Simple Backend (Default)

**Purpose:** Stateless, single-shot code execution

**Implementation:**
- Spawn Podman container with Python image
- Inject code and MCP tool functions into namespace
- Execute via `python -c`
- Capture stdout, stderr, result
- Destroy container

**Characteristics:**
- Fast startup
- No state between calls
- Ideal for data transformation tasks
- Lower memory footprint

**Use cases:**
- Data processing and filtering
- One-off computations
- Format conversions
- Quick analyses

#### Jupyter Backend

**Purpose:** Stateful, multi-step workflows

**Implementation:**
- Spawn Podman container with IPython kernel
- Keep kernel running for session lifetime
- Execute code cells via Jupyter protocol (ZMQ)
- Maintain namespace between executions
- Support rich output formats

**Characteristics:**
- State persists between calls
- Variable persistence
- Interactive workflow support
- Higher resource usage

**Use cases:**
- Multi-step data analysis
- Iterative model training
- Building up complex state
- Exploratory workflows

**Session Management:**
- Sessions identified by unique ID
- Automatic timeout after inactivity (configurable, default: 1 hour)
- Manual cleanup via session deletion
- Resource limits per session

### 3. Custom Environment Building

#### Design Philosophy

**Security Principle:** `pip install` is **NOT** allowed during code execution. All package installations must happen during a controlled build process.

**Why:**
- Prevents malicious package installation during execution
- Ensures reproducible environments
- Allows security scanning of dependencies
- Enables caching for performance
- Provides audit trail of what's installed

#### Build System: UV-Based

**Technology Choice:** [UV](https://github.com/astral-sh/uv) - ultra-fast Python package installer

**Benefits:**
- 10-100x faster than pip
- Built-in caching
- Deterministic dependency resolution
- Lockfile support for reproducibility
- Compatible with pip package specifications

#### Implementation

**Build Process:**

```python
def build_custom_environment(name, packages, base_image="python:3.11"):
    """
    Build a custom container image with specified packages using UV.
    
    Steps:
    1. Generate Containerfile with UV installation
    2. Create requirements specification
    3. Build image with layer caching
    4. Validate and tag image
    5. Store metadata for reuse
    """
    
    # Generate Containerfile
    containerfile = f"""
FROM {base_image}

# Install UV (cached layer)
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.cargo/bin:$PATH"

# Non-root user
RUN useradd -m -u 1000 forge
USER forge

# Copy requirements (cache-friendly)
COPY requirements.txt /tmp/requirements.txt

# Install packages with UV (uses cache)
RUN uv pip install --system -r /tmp/requirements.txt

# MCP bridge client
COPY mcp_tools.py /usr/local/lib/python3.11/site-packages/

WORKDIR /workspace
"""
    
    # Build with Podman
    build_context = create_build_context(containerfile, packages)
    image_id = podman.images.build(
        path=build_context,
        tag=f"mcp-forge/custom:{name}",
        cache_from=[f"mcp-forge/custom:{name}"],
        buildargs={"UV_CACHE_DIR": "/var/cache/mcp-forge/uv"}
    )
    
    return image_id
```

#### Package Cache Strategy

**Multi-Layer Caching:**

```
Layer 1: Base Python image (rarely changes)
  └─ python:3.11-slim

Layer 2: UV installation (stable)
  └─ UV binary + dependencies

Layer 3: Common packages (pre-cached)
  └─ numpy, pandas, requests, etc.
  
Layer 4: User packages (specific to build)
  └─ Custom requirements
```

**UV Cache Directory:**
- Mounted from host: `/var/cache/mcp-forge/uv`
- Persisted between builds
- Dramatically speeds up builds with similar dependencies

**Cache Invalidation:**
- Requirements hash changes → rebuild user layer only
- Base image updated → rebuild from base
- UV version updated → rebuild from UV layer

#### Pre-built Environment Templates

**Common Templates:**

```yaml
templates:
  ml-basic:
    description: "Basic ML stack"
    packages:
      - numpy>=1.24.0
      - pandas>=2.0.0
      - scikit-learn>=1.3.0
    
  ml-advanced:
    description: "Advanced ML with deep learning"
    packages:
      - numpy>=1.24.0
      - pandas>=2.0.0
      - scikit-learn>=1.3.0
      - tensorflow>=2.13.0
      - torch>=2.0.0
    
  data-science:
    description: "Data science stack"
    packages:
      - numpy>=1.24.0
      - pandas>=2.0.0
      - matplotlib>=3.7.0
      - seaborn>=0.12.0
      - jupyter>=1.0.0
    
  web-scraping:
    description: "Web scraping tools"
    packages:
      - requests>=2.31.0
      - beautifulsoup4>=4.12.0
      - lxml>=4.9.0
      - selenium>=4.11.0
```

**Usage:**

```python
# Use a template
build_custom_environment(
  name="my-ml-env",
  template="ml-basic",
  additional_packages=["xgboost>=2.0.0"]
)

# Or build from scratch
build_custom_environment(
  name="custom-env",
  packages=["specific-package==1.0.0"]
)
```

#### Build Validation

**Post-Build Checks:**

```python
def validate_built_environment(image_id):
    """
    Validate that the built environment is safe and functional.
    
    Checks:
    1. All requested packages are installed
    2. No malicious packages (against allowlist/blocklist)
    3. Image size is within limits
    4. Security scan passes (trivy/grype)
    5. Python imports work
    """
    
    # Import test
    test_code = """
import sys
import json
installed = [(pkg.key, pkg.version) for pkg in __import__('pkg_resources').working_set]
print(json.dumps(installed))
"""
    
    result = run_in_container(image_id, test_code)
    installed_packages = json.loads(result.stdout)
    
    # Security scan
    scan_result = security_scan_image(image_id)
    if scan_result.critical_vulns > 0:
        raise SecurityError(f"Image has {scan_result.critical_vulns} critical vulnerabilities")
    
    return {
        "installed_packages": installed_packages,
        "security_scan": scan_result,
        "valid": True
    }
```

#### Package Allowlist/Blocklist

**Security Control:**

```yaml
packages:
  # Automatically allowed (common, vetted)
  allowlist:
    - numpy
    - pandas
    - scikit-learn
    - matplotlib
    - seaborn
    - requests
    # ... many more
  
  # Explicitly forbidden
  blocklist:
    - "os-sys"  # Known malicious
    - "malicious-package"
    # Packages with known vulnerabilities
  
  # Requires manual approval
  manual_approval:
    - "*crypto*"  # Cryptographic packages
    - "*ssh*"     # SSH-related
    - "*subprocess*"  # Process spawning
```

**Validation:**

```python
def validate_package_list(packages):
    """Check packages against allowlist/blocklist before building."""
    for pkg in packages:
        pkg_name = pkg.split("==")[0].split(">=")[0].split("<=")[0]
        
        if pkg_name in BLOCKLIST:
            raise SecurityError(f"Package {pkg_name} is blocked")
        
        if not in_allowlist(pkg_name) and needs_approval(pkg_name):
            raise ApprovalRequiredError(f"Package {pkg_name} requires manual approval")
```

#### Build Resource Limits

**Prevent Build Abuse:**

```python
BUILD_LIMITS = {
    "max_packages": 50,           # Max packages per build
    "max_build_time": 600,        # 10 minutes
    "max_image_size": "2GB",      # Final image size
    "max_concurrent_builds": 3,   # Per user
    "build_rate_limit": {
        "requests": 10,
        "period": 3600  # 10 builds per hour
    }
}
```

#### Environment Lifecycle

**Management:**

```python
# List user's custom environments
list_custom_environments() → [
  {
    "name": "my-ml-env",
    "image": "mcp-forge/custom:my-ml-env",
    "created_at": "2026-02-06T10:00:00Z",
    "size_mb": 1024,
    "last_used": "2026-02-06T11:30:00Z"
  }
]

# Delete unused environment
delete_custom_environment(name="old-env")

# Rebuild environment (e.g., after base image update)
rebuild_custom_environment(name="my-ml-env")
```

**Auto-cleanup:**
- Environments unused for 30 days → archived
- Archived for 90 days → deleted
- User notification before deletion

#### execute_python Integration

**Using Custom Environments:**

```python
# Updated execute_python parameters
{
  "code": "...",
  "custom_image": "string (optional) - Custom environment name or image tag",
  "environment": "string (optional) - Template name (alternative to custom_image)",
  ...
}

# Examples:
execute_python(
  code="import numpy as np; ...",
  custom_image="my-ml-env"  # Use user's custom environment
)

execute_python(
  code="import pandas as pd; ...",
  environment="data-science"  # Use pre-built template
)
```

### 4. Container Runtime (Podman)

#### Why Podman?

- **Rootless by design:** No daemon running as root
- **Docker-compatible API:** Easy integration
- **Better security defaults:** No privileged operations needed
- **Daemonless:** Containers are child processes

#### Deployment Modes

**Development:**
```bash
# Run mcp-forge directly on host
mcp-forge serve --podman-socket /run/user/1000/podman/podman.sock
```

**Production:**
```yaml
# Run mcp-forge in container with socket mount
services:
  mcp-forge:
    image: mcp-forge:latest
    volumes:
      - /run/user/1000/podman/podman.sock:/run/podman/podman.sock:ro
    environment:
      - PODMAN_SOCKET=/run/podman/podman.sock
```

#### Security: Allowlist-Based Container Operations

**Allowed Operations:**

```python
ALLOWED_OPERATIONS = {
    "container.create": {
        "allowed_images": [
            "mcp-forge/python:3.11",
            "mcp-forge/python:3.12",
            "mcp-forge/jupyter:latest",
            "mcp-forge/custom:*"  # User-built custom environments
        ],
        "forbidden_params": [
            "privileged",
            "cap_add",
            "devices",
            "pid_mode",
            "ipc_mode"
        ],
        "required_params": {
            "network_mode": "none",  # Or restricted network
            "read_only": True,       # Filesystem read-only except volumes
            "memory_limit": "512m",
            "cpu_quota": 50000,      # 50% of one CPU
            "security_opt": ["no-new-privileges"],
            "user": "1000:1000"      # Non-root user
        }
    },
    "container.start": {
        "session_containers_only": True  # Only containers we created
    },
    "container.stop": {
        "session_containers_only": True
    },
    "container.remove": {
        "session_containers_only": True
    },
    "container.logs": {
        "session_containers_only": True
    }
}
```

**Forbidden Operations:**

```python
FORBIDDEN_OPERATIONS = [
    "container.exec",      # No direct shell access
    # Note: image.build IS allowed, but ONLY through build_custom_environment tool
    # with validation, security scanning, and package allowlists
    "image.pull",          # No arbitrary image pulling (only pre-approved images)
    "volume.create",       # Only pre-configured volumes
    "network.create",      # No custom networks
    "system.prune",        # No system-level operations
]

# Additionally forbidden within execution containers:
EXECUTION_RESTRICTIONS = [
    "subprocess.run(['pip', 'install', ...])",  # No pip install during execution
    "subprocess.run(['apt', 'install', ...])",  # No system package installs
    "import os; os.system('...')",              # Restricted system calls
]
```

#### Volume Management

**Allowed Volume Patterns:**

```python
ALLOWED_VOLUME_PATTERNS = [
    "/mcp-forge/sessions/{session_id}/*",  # Per-session data
    "/mcp-forge/shared/readonly/*",        # Shared read-only data
    "/mcp-forge/uploads/{session_id}/*"    # User uploads
]

FORBIDDEN_MOUNT_PATHS = [
    "/",
    "/etc",
    "/var/run/docker.sock",
    "/var/run/podman/podman.sock",
    "/sys",
    "/proc"
]
```

#### Resource Limits (Enforced)

```python
RESOURCE_LIMITS = {
    "memory": {
        "default": "512m",
        "max": "2g"
    },
    "cpu_quota": {
        "default": 50000,  # 50% of one CPU
        "max": 100000      # 100% of one CPU
    },
    "timeout": {
        "default": 300,    # 5 minutes
        "max": 1800        # 30 minutes
    },
    "pids_limit": 100,
    "storage": {
        "default": "1g",
        "max": "10g"
    }
}
```

### 5. MCP Tool Injection

#### Tool Discovery Flow

1. **Agent reads resource:** `mcp://forge/tools/available`
2. **MCP-Forge returns:** `["github_search_repos", "filesystem_read"]`
3. **Agent knows signatures:** Already configured in agent's tool config
4. **Agent generates code:** Using these tool names as Python functions

#### Tool Injection Implementation

**At container startup:**

```python
# MCP-Forge generates injection code
def inject_mcp_tools(container, mcp_client, tool_names):
    """
    Inject MCP tools as Python functions into container namespace.
    
    Args:
        container: Podman container instance
        mcp_client: Connected MCP client with available tools
        tool_names: List of tool names to inject
    """
    
    injection_code = """
# MCP Tool Functions (auto-injected)
import json
from typing import Any

def _mcp_call(tool_name: str, **kwargs) -> Any:
    '''Internal: Call MCP tool via bridge'''
    # This communicates with MCP-Forge server which forwards to MCP client
    import socket
    s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
    s.connect('/tmp/mcp-bridge.sock')
    s.send(json.dumps({'tool': tool_name, 'params': kwargs}).encode())
    result = json.loads(s.recv(1024*1024).decode())
    s.close()
    return result

"""
    
    # Generate wrapper function for each tool
    for tool_name in tool_names:
        tool_schema = mcp_client.get_tool_schema(tool_name)
        
        # Generate function signature from schema
        params = tool_schema.get('inputSchema', {}).get('properties', {})
        param_str = ', '.join(f"{k}: Any = None" for k in params.keys())
        
        injection_code += f"""
def {tool_name}({param_str}):
    '''
    {tool_schema.get('description', '')}
    
    Auto-generated wrapper for MCP tool: {tool_name}
    '''
    kwargs = {{{', '.join(f"'{k}': {k}" for k in params.keys())}}}
    kwargs = {{k: v for k, v in kwargs.items() if v is not None}}
    return _mcp_call('{tool_name}', **kwargs)

"""
    
    # Write to container's Python site-packages or PYTHONSTARTUP
    container.write_file('/usr/local/lib/python3.11/site-packages/mcp_tools.py', 
                         injection_code)
    
    # Start MCP bridge server (forwards calls to actual MCP client)
    start_mcp_bridge(container, mcp_client)
```

**Alternative: Environment Variable Approach**

```python
# Set PYTHONSTARTUP to auto-import tools
container.env['PYTHONSTARTUP'] = '/mcp-forge/startup.py'
```

#### MCP Bridge Server

A lightweight server running in the MCP-Forge process that:
- Listens on Unix socket mounted into container
- Receives tool calls from container code
- Forwards to actual MCP client
- Returns results to container

This keeps the MCP client logic outside the untrusted container.

### 6. Session State Management

#### State Lifecycle

**Stateless Execution (session_id=None):**
```
Request → Create Container → Execute → Return Results → Destroy Container
```

**Stateful Execution (session_id="abc123"):**
```
First Request:
  Request → Create Container → Start Kernel → Execute → Return Results
  Container stays alive

Subsequent Requests:
  Request → Reuse Container → Execute in Same Kernel → Return Results
  
Cleanup:
  Timeout or Manual → Stop Container → Destroy Container
```

#### State Documentation

**Agent Workflow:**

```python
# Step 1: Load and process data
execute_python(
  code="df = load_large_dataset(); df_clean = preprocess(df)",
  session_id="analysis-123",
  backend="jupyter"
)

# Step 2: Document important state
document_state(
  session_id="analysis-123",
  variables={
    "df": "Raw dataset, 100k rows",
    "df_clean": "Cleaned dataset, 95k rows, ready for modeling"
  },
  note="Data loading complete"
)

# ... conversation continues, context window rotates ...

# Step 3: Agent checks state after context rotation
state = read_resource("mcp://forge/sessions/analysis-123/state")
# Agent sees: df_clean exists and is ready

# Step 4: Continue work
execute_python(
  code="model = train_model(df_clean); results = evaluate(model)",
  session_id="analysis-123"
)
```

#### State Introspection

**Automatic tracking:**
- Kernel namespace inspection (all variables)
- Variable types and sizes
- Last execution timestamp

**Agent-documented:**
- Semantic descriptions
- Workflow notes
- Important variables highlighted

**Combined in resource:**
```json
{
  "all_variables": ["df", "df_clean", "model", "temp", "i", "results"],
  "documented_variables": {
    "df_clean": "Cleaned dataset, 95k rows, ready for modeling",
    "model": "Trained model, 87% accuracy",
    "results": "Evaluation metrics"
  },
  "introspection": {
    "df_clean": {"type": "DataFrame", "shape": [95000, 12], "memory_mb": 87},
    "model": {"type": "RandomForestClassifier", "memory_mb": 234}
  },
  "note": "Ready for final predictions",
  "last_updated": "2026-02-06T10:30:00Z"
}
```

## Implementation Considerations

### Technology Stack

**MCP Server:**
- Language: Python 3.11+
- Framework: `mcp` SDK (official Python implementation)
- Async: `asyncio` for concurrent operations

**Container Runtime:**
- Podman via `podman-py` library
- Fallback: Direct Podman CLI calls

**Jupyter Backend:**
- `jupyter_client` for kernel management
- ZMQ for communication
- `ipykernel` in container images

**Simple Backend:**
- Direct Python execution via `subprocess`
- Or: `python:3.11-slim` base image with `exec` entrypoint

### Pre-built Container Images

**Base Image: `mcp-forge/python:3.11`**

```dockerfile
FROM python:3.11-slim

# Non-root user
RUN useradd -m -u 1000 forge
USER forge

# Install common packages
RUN pip install --user numpy pandas requests

# MCP bridge client
COPY mcp_tools.py /usr/local/lib/python3.11/site-packages/

# Startup script
COPY startup.py /mcp-forge/startup.py

WORKDIR /workspace
```

**Jupyter Image: `mcp-forge/jupyter:latest`**

```dockerfile
FROM mcp-forge/python:3.11

USER root
RUN pip install ipykernel jupyter_client
USER forge

# IPython config for security
COPY ipython_config.py /home/forge/.ipython/profile_default/

CMD ["python", "-m", "ipykernel_launcher", "-f", "/tmp/kernel.json"]
```

### Error Handling

**Container Failures:**
- Timeout: Kill container, return timeout error
- OOM: Return memory limit error with suggestion to increase
- Crash: Return stderr and exit code

**MCP Tool Failures:**
- Tool not available: Clear error message with available tools
- Tool call error: Return tool error to agent, don't crash execution
- Network error: Retry logic for transient failures

**Security Violations:**
- Forbidden operation: Reject immediately, log to audit
- Resource limit exceeded: Terminate execution, clear error message
- Invalid volume mount: Reject with explanation

### Performance Considerations

**Container Reuse:**
- Pool of warm containers for simple backend (optional)
- Lazy cleanup of idle Jupyter sessions

**Parallel Execution:**
- Support multiple concurrent executions
- Per-session locking for stateful operations
- Configurable max concurrent containers

**Image Caching:**
- Pre-pull images on startup
- Periodic image updates (configurable schedule)

**Volume Performance:**
- Use tmpfs for ephemeral data
- Persistent volumes for session data
- Cleanup strategy for old sessions

## Security Model

### Defense in Depth

1. **Podman rootless:** No root daemon
2. **Allowlist enforcement:** Only permitted operations
3. **Read-only filesystem:** Except specific volumes
4. **Network isolation:** No internet by default
5. **Resource limits:** CPU, memory, storage, PIDs
6. **No privileged mode:** Ever
7. **Capability dropping:** Minimal capabilities
8. **User namespaces:** Non-root user in container
9. **Audit logging:** All operations logged
10. **Session isolation:** Each session has isolated volumes

### Threat Model

**Threats Mitigated:**
- Container escape → Rootless + restricted operations
- Resource exhaustion → Hard limits enforced
- Data exfiltration → Network isolation
- Privilege escalation → No privileged mode, capability restrictions
- Host filesystem access → Allowlist volumes only

**Out of Scope:**
- Side-channel attacks (Spectre, Meltdown)
- Physical security
- Supply chain attacks on base images (use trusted registries)

### Audit and Monitoring

**Logged Events:**
- Container create/start/stop/remove
- Execution requests (code hash, not full code for privacy)
- Resource limit violations
- Security policy violations
- Session lifecycle events

**Log Format:**
```json
{
  "timestamp": "2026-02-06T10:30:00Z",
  "event": "container.create",
  "session_id": "abc123",
  "image": "mcp-forge/python:3.11",
  "resources": {"memory": "512m", "cpu_quota": 50000},
  "success": true
}
```

## Configuration

### Server Configuration

**Example `mcp-forge.yaml`:**

```yaml
server:
  host: localhost
  port: 3000
  podman_socket: /run/user/1000/podman/podman.sock

execution:
  default_backend: simple
  default_timeout: 300
  max_timeout: 1800
  default_memory: 512m
  max_memory: 2g
  default_cpu_quota: 50000
  max_cpu_quota: 100000

images:
  python_3_11: mcp-forge/python:3.11
  python_3_12: mcp-forge/python:3.12
  jupyter: mcp-forge/jupyter:latest
  auto_pull: true
  pull_interval: 86400  # 24 hours

sessions:
  idle_timeout: 3600  # 1 hour
  max_concurrent: 10
  cleanup_interval: 300  # 5 minutes

volumes:
  base_path: /var/lib/mcp-forge
  session_quota: 1g
  max_session_quota: 10g

security:
  audit_log: /var/log/mcp-forge/audit.log
  enforce_resource_limits: true
  allow_network: false

environment_builder:
  enabled: true
  uv_cache_path: /var/cache/mcp-forge/uv
  max_packages: 50
  max_build_time: 600
  max_image_size: 2GB
  templates_path: /etc/mcp-forge/templates

mcp_tools:
  # MCP servers to connect to and expose
  github:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-github"]
    env:
      GITHUB_TOKEN: ${GITHUB_TOKEN}
  
  filesystem:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/allowed/path"]
```

### Environment Variables

```bash
# Podman socket location
MCP_FORGE_PODMAN_SOCKET=/run/user/1000/podman/podman.sock

# Base path for volumes
MCP_FORGE_VOLUMES_PATH=/var/lib/mcp-forge

# UV cache for fast package installation
MCP_FORGE_UV_CACHE_PATH=/var/cache/mcp-forge/uv

# Security
MCP_FORGE_AUDIT_LOG=/var/log/mcp-forge/audit.log

# MCP tool credentials
GITHUB_TOKEN=ghp_xxxxx
```

## API Examples

### Example 1: Simple Data Processing

```python
# Agent discovers available tools
tools = read_resource("mcp://forge/tools/available")
# Returns: ["github_search_repos", "filesystem_read"]

# Agent generates and executes code
result = execute_python(
  code="""
repos = github_search_repos(query="machine learning", max_results=100)
python_repos = [r for r in repos['items'] if r['language'] == 'Python']
top_10 = sorted(python_repos, key=lambda x: x['stars'], reverse=True)[:10]

# Return summary
[{
  'name': r['name'],
  'stars': r['stars'],
  'url': r['html_url']
} for r in top_10]
""",
  mcp_tools=["github_search_repos"],
  session_id=None
)

print(result['result'])
# Returns top 10 Python ML repos
```

### Example 2: Building and Using Custom Environment

```python
# Agent needs ML libraries for analysis
# First, check available environments
envs = read_resource("mcp://forge/environments/list")

# Build custom environment if needed
build_result = build_custom_environment(
  name="my-ml-analysis",
  packages=[
    "numpy>=1.24.0",
    "pandas>=2.0.0",
    "scikit-learn>=1.3.0",
    "xgboost>=2.0.0",
    "matplotlib>=3.7.0",
    "seaborn>=0.12.0"
  ],
  description="Custom ML environment for analysis project"
)

# Wait for build to complete (typically 30-60 seconds with UV cache)
print(f"Built {build_result['image_name']} in {build_result['build_time']}s")
print(f"Installed: {build_result['installed_packages']}")

# Now use the custom environment for analysis
result = execute_python(
  code="""
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import xgboost as xgb

# Load data (from volume or MCP tool)
data = filesystem_read('/data/customer_churn.csv')
df = pd.read_csv(data)

# Preprocess
X = df.drop('churn', axis=1)
y = df['churn']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train models
rf_model = RandomForestClassifier(n_estimators=100)
rf_model.fit(X_train, y_train)
rf_score = rf_model.score(X_test, y_test)

xgb_model = xgb.XGBClassifier()
xgb_model.fit(X_train, y_train)
xgb_score = xgb_model.score(X_test, y_test)

# Return results
{
  'random_forest_accuracy': rf_score,
  'xgboost_accuracy': xgb_score,
  'best_model': 'xgboost' if xgb_score > rf_score else 'random_forest'
}
""",
  custom_image="my-ml-analysis",
  mcp_tools=["filesystem_read"],
  session_id="churn-analysis"
)

print(result['result'])
# Returns: {'random_forest_accuracy': 0.87, 'xgboost_accuracy': 0.91, 'best_model': 'xgboost'}
```

### Example 3: Stateful Analysis with Custom Environment

```python
# Create session with custom environment
session_id = "deep-analysis"

# Step 1: Load and explore data
execute_python(
  code="""
import pandas as pd
import matplotlib.pyplot as plt

df = filesystem_read_csv('/data/sales_2024.csv')
print(f"Loaded {len(df)} rows")
print(df.describe())

# Store for later
df.to_pickle('/tmp/sales_df.pkl')
""",
  custom_image="data-science",  # Use pre-built template
  mcp_tools=["filesystem_read_csv"],
  session_id=session_id,
  backend="jupyter"
)

# Document what we have
document_state(
  session_id=session_id,
  variables={
    "df": "Sales data for 2024, loaded and stored as pickle"
  },
  note="Data loaded and ready for analysis"
)

# ... conversation continues, context rotates ...

# Step 2: Later, continue analysis
state = read_resource(f"mcp://forge/sessions/{session_id}/state")
# Agent sees df is available

execute_python(
  code="""
import pandas as pd
df = pd.read_pickle('/tmp/sales_df.pkl')

# Analyze trends
monthly_sales = df.groupby('month')['revenue'].sum()
growth_rate = monthly_sales.pct_change().mean() * 100

{
  'total_revenue': float(df['revenue'].sum()),
  'avg_monthly_growth': float(growth_rate),
  'top_products': df.groupby('product')['revenue'].sum().nlargest(5).to_dict()
}
""",
  session_id=session_id
)
```

### Example 4: Using Pre-built Templates

```python
# Quick start with pre-built template
result = execute_python(
  code="""
import requests
from bs4 import BeautifulSoup

# Scrape some data
response = requests.get('https://example.com/data')
soup = BeautifulSoup(response.content, 'lxml')

# Extract and process
data = [item.text for item in soup.find_all('div', class_='data-item')]
data[:10]  # Return first 10 items
""",
  environment="web-scraping",  # Use template instead of building custom
  session_id=None
)

print(result['result'])
```

## Implementation Roadmap

### Phase 1: Core Execution (MVP)
- Basic execute_python tool with simple backend
- Podman integration with security restrictions
- MCP tool injection
- Pre-built Python 3.11 image

### Phase 2: Stateful Execution
- Jupyter backend implementation
- Session management
- State documentation tool
- Session resources

### Phase 3: Custom Environments
- build_custom_environment tool
- UV-based package installation
- Build caching
- Package allowlist/blocklist
- Security validation

### Phase 4: Advanced Features
- Pre-built template library
- Environment lifecycle management
- Build rate limiting
- Advanced audit logging
- Multi-user support with quotas

### Phase 5: Production Hardening
- Performance optimization
- Advanced security scanning
- Comprehensive monitoring
- High availability setup
- Documentation and examples

### Example 5: Multi-Step Stateful Analysis (Original Approach)

```python
# Step 1: Load data
execute_python(
  code="""
data = filesystem_read('/data/sales.csv')
import pandas as pd
df = pd.read_csv(io.StringIO(data))
print(f"Loaded {len(df)} rows")
""",
  session_id="analysis-001",
  backend="jupyter",
  mcp_tools=["filesystem_read"]
)

# Step 2: Document state
document_state(
  session_id="analysis-001",
  variables={
    "df": "Sales data, 50k rows, columns: date, product, amount, region"
  },
  note="Data loaded, ready for analysis"
)

# Step 3: Process (in same session)
execute_python(
  code="""
# df still exists from previous execution
monthly_sales = df.groupby(df['date'].dt.month)['amount'].sum()
monthly_sales.plot()
plt.savefig('/workspace/output/monthly.png')
monthly_sales.to_dict()  # Return summary
""",
  session_id="analysis-001"
)

# Step 4: Check state later
state = read_resource("mcp://forge/sessions/analysis-001/state")
# See what variables exist
```

### Example 6: Tool Error Handling

```python
result = execute_python(
  code="""
try:
    repos = github_search_repos(query="test", max_results=1000)
except Exception as e:
    print(f"GitHub API error: {e}")
    repos = {'items': []}  # Fallback

len(repos['items'])
""",
  mcp_tools=["github_search_repos"]
)

# Agent sees stdout with error message but execution continues
```

## Deployment

### Development Setup

```bash
# Install Podman (rootless)
sudo apt install podman
podman system migrate  # Enable rootless

# Install MCP-Forge
pip install mcp-forge

# Build container images
mcp-forge build-images

# Start server
mcp-forge serve --config mcp-forge.yaml
```

### Production Deployment

**Docker Compose (using Podman):**

```yaml
version: '3.8'

services:
  mcp-forge:
    image: mcp-forge:latest
    volumes:
      - /run/user/1000/podman/podman.sock:/run/podman/podman.sock:ro
      - ./mcp-forge.yaml:/etc/mcp-forge/config.yaml:ro
      - mcp-forge-data:/var/lib/mcp-forge
      - mcp-forge-logs:/var/log/mcp-forge
    environment:
      - GITHUB_TOKEN=${GITHUB_TOKEN}
    restart: unless-stopped
    user: "1000:1000"

volumes:
  mcp-forge-data:
  mcp-forge-logs:
```

**Systemd Service:**

```ini
[Unit]
Description=MCP-Forge Server
After=podman.socket

[Service]
Type=simple
User=forge
Environment=MCP_FORGE_PODMAN_SOCKET=/run/user/1000/podman/podman.sock
ExecStart=/usr/local/bin/mcp-forge serve --config /etc/mcp-forge/config.yaml
Restart=on-failure

[Install]
WantedBy=multi-user.target
```

### Monitoring

**Metrics to track:**
- Active sessions count
- Container count (running, idle)
- Execution duration (p50, p95, p99)
- Memory usage per container
- CPU usage per container
- Failed executions (rate, reasons)
- Security violations (rate, types)

**Health Checks:**
- Podman socket accessible
- Container image availability
- Volume filesystem writable
- MCP tool connectivity

## Future Enhancements

### Planned Features

1. **Language Support:**
   - JavaScript/Node.js execution
   - R execution
   - Shell script execution

2. **Advanced Volumes:**
   - S3/blob storage integration
   - Shared volumes between sessions
   - Volume snapshots

3. **Networking:**
   - Opt-in restricted internet access
   - Allowlist for specific domains
   - VPN/proxy support

4. **Collaboration:**
   - Shared sessions between agents
   - Session forking
   - Session snapshots/restore

5. **Performance:**
   - GPU support for ML workloads
   - Container warm pools
   - Compilation caching (for numba, etc.)

6. **Observability:**
   - Execution tracing
   - Performance profiling
   - Resource usage visualization

### Open Questions

1. **Long-Running Tasks:**
   - Background job execution?
   - Streaming results during execution?
   - Callback URLs for completion?

2. **Data Sharing:**
   - Session-to-session data transfer?
   - Export results to external storage?
   - Import data from URLs?

3. **Cost Management:**
   - Resource usage quotas per user?
   - Billing integration?
   - Fair scheduling?

## Conclusion

MCP-Forge provides a secure, efficient way for AI agents to execute code with access to MCP tools, solving the problem of large data payloads in LLM contexts. The architecture prioritizes security through defense-in-depth while maintaining flexibility for both simple and complex workflows.

Key design principles:
- **Security first:** Rootless containers, allowlists, resource limits
- **Agent-friendly:** Resources for discovery, state documentation
- **Flexible:** Stateless and stateful execution modes
- **Efficient:** Process data locally, return only results
- **Observable:** Comprehensive audit logging and monitoring

The architecture is designed for implementation with clear component boundaries, well-defined interfaces, and security considerations baked in from the start.