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docs/HTTP_TRANSPORT.md

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+# HTTP Transport Configuration Example
+
+This example shows how to configure MCP-Forge to connect to external HTTP-based MCP servers.
+
+## Configuration File
+
+```yaml
+# config.yaml
+server:
+  host: localhost
+  port: 3000
+  podman_socket: /run/user/1000/podman/podman.sock
+
+security:
+  audit_log: ./logs/audit.log
+  enforce_resource_limits: true
+  allow_network: false
+
+execution:
+  default_backend: simple
+  default_timeout: 300
+  max_timeout: 1800
+  default_memory: 512m
+  max_memory: 2g
+  base_image: docker.io/library/python:3.13-slim
+
+environment_builder:
+  enabled: true
+  uv_cache_path: ./cache/uv
+  max_packages_per_build: 50
+  max_build_time: 600
+  max_image_size: 2147483648
+  max_concurrent_builds: 3
+  build_rate_limit:
+    requests: 10
+    period: 60
+  package_validation:
+    use_allowlist: true
+    allowlist_path: ./config/allowlist.txt
+    blocklist_path: ./config/blocklist.txt
+    require_approval_patterns: []
+
+# Connect to external MCP servers
+mcp_tools:
+  # HTTP-based MCP server
+  remote_api:
+    transport: http
+    url: http://localhost:8006/mcp
+    headers:
+      Authorization: "Bearer your-token-here"
+      X-Custom-Header: "value"
+  
+  # SSE-based MCP server
+  sse_service:
+    transport: sse
+    url: http://localhost:9000/events
+    headers:
+      Authorization: "Bearer your-token-here"
+  
+  # Traditional stdio-based MCP server (still supported)
+  filesystem:
+    transport: stdio  # default, can be omitted
+    command: uvx
+    args: [mcp-server-filesystem, /path/to/workspace]
+    env:
+      SOME_VAR: value
+```
+
+## Transport Types
+
+### 1. HTTP Transport
+
+Used for HTTP-based MCP servers that communicate via HTTP requests.
+
+```yaml
+remote_api:
+  transport: http
+  url: http://localhost:8006/mcp
+  headers:
+    Authorization: "Bearer token123"
+    Content-Type: "application/json"
+```
+
+**Required:**
+- `url`: HTTP endpoint URL
+
+**Optional:**
+- `headers`: HTTP headers (dict)
+
+### 2. SSE Transport
+
+Used for Server-Sent Events (SSE) based MCP servers.
+
+```yaml
+sse_service:
+  transport: sse
+  url: http://localhost:9000/events
+  headers:
+    Authorization: "Bearer token123"
+```
+
+**Required:**
+- `url`: SSE endpoint URL
+
+**Optional:**
+- `headers`: HTTP headers (dict)
+
+### 3. Stdio Transport (Default)
+
+Traditional command-based MCP servers.
+
+```yaml
+filesystem:
+  transport: stdio  # default, can be omitted
+  command: uvx
+  args: [mcp-server-filesystem, /workspace]
+  env:
+    PATH: /usr/bin
+```
+
+**Required:**
+- `command`: Executable command
+
+**Optional:**
+- `args`: Command arguments (list)
+- `env`: Environment variables (dict)
+
+## Running the Server
+
+```bash
+# Start with HTTP transport configuration
+uv run mcp-forge --config config.yaml
+
+# Or with SSE transport
+uv run mcp-forge --config config.yaml --transport sse --host 0.0.0.0 --port 8080
+```
+
+## Client Connection
+
+Once the server is running, MCP clients can connect using the configured transport:
+
+```python
+from fastmcp import Client
+from fastmcp.client.transports import StreamableHttpTransport
+
+# Connect to MCP-Forge server via HTTP
+transport = StreamableHttpTransport(url="http://localhost:3000/mcp")
+client = Client(transport)
+
+async with client:
+    # List available tools (from all configured MCP servers)
+    tools = await client.list_tools()
+    print(tools)
+```
+
+## Testing the Connection
+
+You can test your HTTP transport configuration using curl:
+
+```bash
+# Test HTTP endpoint
+curl -X POST http://localhost:8006/mcp \
+  -H "Authorization: Bearer your-token-here" \
+  -H "Content-Type: application/json" \
+  -d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'
+```
+
+## Troubleshooting
+
+### Connection Refused
+- Verify the external MCP server is running
+- Check the URL and port are correct
+- Ensure firewall rules allow the connection
+
+### Authentication Errors
+- Verify the authorization header is correct
+- Check if the external server requires specific headers
+
+### Tool Not Found
+- Ensure the external MCP server exposes the expected tools
+- Check the server logs for any errors

docs/development-methodology.md

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+# AI-Assisted Software Development: A Methodology for Rapid, Test-Driven Implementation
+
+## Abstract
+
+This document describes a development methodology that combines architectural planning, test-driven development, and AI-assisted implementation to achieve rapid yet robust software construction. Using the mcp-forge project as a case study (5,325 lines of production code developed in one day), we demonstrate how structured planning and clear acceptance criteria enable effective human-AI collaboration while maintaining code quality and architectural integrity.
+
+## Introduction
+
+Traditional software development faces a fundamental tension: moving fast often compromises quality, while maintaining quality slows development. AI-assisted development promises to resolve this tension, but requires methodological discipline to avoid producing technically functional yet architecturally weak systems.
+
+The methodology described here was validated through building mcp-forge, a production-grade secure Python execution server with MCP protocol integration, achieving:
+- **5,325 lines of code** in a single development session
+- **388 passing tests** (100% test coverage of core functionality)
+- **Zero architectural rework** required post-implementation
+- **Production-ready security** (container isolation, audit logging, resource limits)
+
+## Methodology Overview
+
+The approach consists of four sequential phases, each building upon the previous:
+
+```
+1. Architecture Design
+   ↓
+2. Test-Driven Work Planning
+   ↓
+3. Guided AI Implementation
+   ↓
+4. Integration Validation
+```
+
+### Phase 1: Architecture-First Planning
+
+**Objective**: Establish system structure before writing any code.
+
+**Process**:
+1. Create an architecture document (`architecture.md`) containing:
+   - System overview and objectives
+   - Component breakdown with responsibilities
+   - Data flow diagrams
+   - Technology stack decisions
+   - Security considerations
+   - Integration points
+
+2. Focus on **interfaces over implementation**:
+   - Define contracts between components
+   - Specify data structures
+   - Identify abstraction boundaries
+
+3. Make **technology choices explicit**:
+   - State assumptions and constraints
+   - Document why alternatives were rejected
+   - Note potential technical risks
+
+**Example from mcp-forge**:
+
+```markdown
+## Component: Execution Backend
+
+**Responsibility**: Execute Python code in isolated containers
+
+**Interface**:
+- `execute(code: str, timeout: int, memory: str) -> ExecutionResult`
+
+**Implementation Options**:
+1. Simple: Stateless execution (chosen for MVP)
+2. Jupyter: Stateful sessions (Phase 2)
+
+**Security Requirements**:
+- No network access by default
+- Resource limits enforced
+- Container isolation mandatory
+```
+
+**Key Principle**: The architecture document serves as a contract between human designer and AI implementer. Ambiguity here multiplies into implementation uncertainty.
+
+### Phase 2: Test-Driven Work Planning
+
+**Objective**: Convert architecture into actionable tasks with verifiable completion criteria.
+
+**Process**:
+1. Create a development plan (`todo.md`) structured as:
+   - Phases (major milestones)
+   - Tasks (implementable units)
+   - Acceptance criteria (objective success metrics)
+
+2. **Enforce bottom-up development**:
+   - Start with foundational components (no dependencies)
+   - Build progressively toward integration
+   - Each layer tested before next begins
+
+3. **Write acceptance criteria that prevent shortcuts**:
+   - Require specific test coverage
+   - Mandate error handling
+   - Specify edge cases
+   - Include performance requirements
+
+4. **Make testing non-optional**:
+   - Every task includes "Tests written and passing"
+   - Integration tasks require integration tests
+   - No task complete without verification
+
+**Example Task Structure**:
+
+```markdown
+## Phase 1: Foundation
+
+### Task 1.1: Configuration Schema
+- [ ] Create Pydantic models for all config sections
+- [ ] Validate YAML parsing
+- [ ] Handle missing/invalid configurations
+- [ ] **Tests**: Config validation tests (>90% coverage)
+- [ ] **Acceptance**: All edge cases handled, no runtime config errors
+
+### Task 1.2: Podman Client Wrapper
+- [ ] Implement container create/start/stop/remove
+- [ ] Handle connection errors gracefully
+- [ ] Add timeout protection
+- [ ] **Tests**: Unit tests with mocked Podman API
+- [ ] **Acceptance**: All Podman operations covered, error paths tested
+```
+
+**Anti-pattern Warning**: Vague criteria like "Implement X" or "Make Y work" lead to incomplete implementations. The AI will declare success prematurely without specific verification requirements.
+
+### Phase 3: Guided AI Implementation
+
+**Objective**: Leverage AI for rapid implementation while maintaining human control over design decisions.
+
+**Human Role**:
+- **Architect**: Make design choices when ambiguity exists
+- **Reviewer**: Validate implementations against architecture
+- **Course-corrector**: Intervene when AI diverges from requirements
+- **Preference-setter**: Override AI's default choices when alternatives better suit your needs
+
+**AI Role**:
+- **Implementer**: Write code following specifications
+- **Test-writer**: Create comprehensive test coverage
+- **Problem-solver**: Debug issues and propose solutions
+- **Documenter**: Generate docstrings and comments
+
+**Collaboration Pattern**:
+
+```
+Human: [Reviews architecture] → [Creates task with acceptance criteria]
+   ↓
+AI: [Implements task] → [Writes tests] → [Runs tests]
+   ↓
+Human: [Validates approach] → [Accepts OR provides feedback]
+   ↓
+AI: [Refines if needed] → [Marks task complete]
+```
+
+**When to Intervene**:
+
+1. **Design Disagreement**: AI chooses approach that conflicts with architecture
+   - *Example*: "Use threads instead of async for the bridge server"
+   - *Action*: Redirect to architectural decision
+
+2. **Preference Mismatch**: Implementation works but doesn't match your style
+   - *Example*: "I prefer explicit error handling over exceptions here"
+   - *Action*: Request specific changes
+
+3. **Incomplete Coverage**: AI claims completion but acceptance criteria not met
+   - *Example*: "Task complete" but edge case tests missing
+   - *Action*: Point to specific uncovered scenarios
+
+4. **Over-engineering**: AI adds unnecessary complexity
+   - *Example*: Elaborate caching when simple lookup sufficient
+   - *Action*: Request simplification
+
+**When NOT to Intervene**:
+- Implementation details within architectural constraints
+- Naming conventions (unless critical to domain)
+- Code organization within modules
+- Test structure (if coverage is adequate)
+
+### Phase 4: Integration Validation
+
+**Objective**: Verify components work together as designed.
+
+**Trigger**: As soon as two or more components interact.
+
+**Process**:
+
+1. **Write integration tests immediately**:
+   ```python
+   def test_execute_with_audit_logging():
+       """Integration: Execution + Audit"""
+       result = backend.execute(code="print('test')")
+       assert result.success
+       
+       # Verify audit log entry created
+       logs = audit_logger.get_recent()
+       assert any(log.event_type == "EXECUTION" for log in logs)
+   ```
+
+2. **Test failure paths**:
+   - Network errors during MCP communication
+   - Container crashes during execution
+   - Resource limit violations
+
+3. **Validate end-to-end flows**:
+   - Complete user scenarios from entry to exit
+   - Cross-component data flow
+   - State consistency across boundaries
+
+4. **Performance validation**:
+   - Measure actual execution times
+   - Verify resource cleanup
+   - Check memory leaks
+
+**Critical Insight**: Unit tests verify components work in isolation; integration tests verify your architecture is correct. Both are mandatory.
+
+## Case Study: MCP Tool Injection Feature
+
+This feature demonstrates the methodology in practice:
+
+### Architecture Decision
+```markdown
+**Requirement**: Python code in containers must call rag-mcp tools
+
+**Design**: Unix socket bridge
+- Bridge server runs on host
+- Socket mounted into containers
+- Python wrapper functions generated dynamically
+- JSON-RPC over socket
+```
+
+### Task Breakdown
+```markdown
+1. MCPClientManager: Connect to external MCP servers
+2. ToolBridgeServer: Unix socket server forwarding calls
+3. ToolInjectionGenerator: Generate Python wrapper code
+4. Integration: Volume mounting + code injection
+```
+
+### Guided Implementation
+
+**Iteration 1**: AI used threading for bridge server
+- **Human intervention**: "Use asyncio instead, server already async"
+- **Result**: Full async rewrite
+
+**Iteration 2**: Root() serialization returned empty dicts
+- **AI debugging**: Discovered FastMCP wraps data in Root objects
+- **Solution**: Parse from `content[0].text` instead
+- **Human validation**: "Test with real rag-mcp call"
+
+**Iteration 3**: Integration test
+```python
+def test_tool_injection_end_to_end():
+    result = execute_python(
+        code='''
+docs = browse_documents(page_size=2)
+print(f"Retrieved {len(docs)} documents")
+        ''',
+        mcp_tools=['browse_documents']
+    )
+    assert "Retrieved 2 documents" in result.stdout
+```
+
+**Result**: Working in 2 seconds per call, 100 records retrieved without context pollution.
+
+## Benefits and Limitations
+
+### Benefits
+
+1. **Speed**: 5,000+ LOC in one day without sacrificing quality
+2. **Quality**: Test-driven approach forces correctness
+3. **Architecture**: Planning phase prevents structural rework
+4. **Maintainability**: Clear separation of concerns, well-documented
+5. **Flexibility**: Easy to adjust during development (change architecture → update tasks → re-implement)
+
+### Limitations
+
+1. **Requires Domain Knowledge**: Human must understand the problem space
+   - *Mitigated by*: AI can explain concepts, but cannot design unfamiliar systems
+
+2. **Architecture Skills Critical**: Poor initial design leads to rework
+   - *Mitigated by*: Start with high-level design, refine before implementation
+
+3. **Acceptance Criteria Must Be Precise**: Vague criteria → incomplete implementation
+   - *Mitigated by*: Include specific tests and edge cases in task descriptions
+
+4. **AI Can't Resolve Ambiguity**: Will make assumptions that may not align with intent
+   - *Mitigated by*: Review implementations actively, provide feedback early
+
+### Comparison with Traditional Development
+
+| Aspect | Traditional | This Methodology | Speedup |
+|--------|------------|------------------|---------|
+| Planning | 1-2 days | 2-3 hours | 3-4x |
+| Implementation | 2-3 weeks | 1 day | 10-15x |
+| Testing | 2-3 days | Concurrent | 2-3x |
+| Documentation | 1-2 days | Concurrent | ∞ |
+| **Total** | **3-4 weeks** | **1-2 days** | **15-20x** |
+
+*Note: Assumes experienced developer familiar with domain*
+
+## Best Practices
+
+### DO:
+
+1. **Invest in architecture**: 2-3 hours planning saves days of rework
+2. **Write specific acceptance criteria**: Include edge cases, error conditions
+3. **Test continuously**: Don't accumulate untested code
+4. **Intervene early**: Small course corrections prevent large detours
+5. **Document decisions**: Capture "why" not just "what"
+
+### DON'T:
+
+1. **Skip planning**: "Start coding and figure it out" fails with AI
+2. **Accept vague completions**: AI will claim success too early
+3. **Batch testing**: Test each component before building next
+4. **Over-specify implementation**: Allow AI freedom within constraints
+5. **Ignore integration testing**: Unit tests alone miss architectural issues
+
+## Applicability
+
+This methodology works best for:
+
+### Ideal Projects:
+- ✅ Well-defined requirements
+- ✅ Known technology stack
+- ✅ Experienced human architect
+- ✅ Clear success criteria
+- ✅ 1,000-10,000 LOC scale
+
+### Less Suitable For:
+- ❌ Research/exploratory projects
+- ❌ Novel algorithm development
+- ❌ UI/UX heavy applications
+- ❌ Undefined requirements
+- ❌ Extreme performance optimization
+
+## Future Directions
+
+Potential methodology enhancements:
+
+1. **Formal Verification**: Use AI to generate formal specifications from architecture
+2. **Automated Architecture Validation**: Check implementations against architectural constraints
+3. **Progressive Refinement**: Start with high-level design, AI proposes detailed architecture
+4. **Multi-Agent Collaboration**: Separate AI agents for architecture, implementation, testing
+
+## Conclusion
+
+AI-assisted development can achieve 10-20x speedups over traditional development when combined with:
+1. Upfront architectural planning
+2. Test-driven task breakdown
+3. Human oversight on design decisions
+4. Continuous integration validation
+
+The key insight is that **AI excels at implementation but requires human guidance on architecture**. By clearly separating these concerns and establishing objective acceptance criteria, we can leverage AI's speed while maintaining human control over system design.
+
+The mcp-forge case study demonstrates this is not theoretical: 5,325 lines of production-quality code, fully tested, with zero architectural rework, in a single day of development. This represents a paradigm shift in how software can be built.
+
+## References
+
+**Project**: mcp-forge - Secure Python Execution Server with MCP Support
+- **Repository**: [URL]
+- **LOC**: 5,325 (source), 388 tests
+- **Development Time**: 1 day (single human + AI)
+- **Technology**: Python, FastMCP, Podman, asyncio
+- **Complexity**: Multi-component system with security, async I/O, container orchestration
+
+**Artifacts**:
+- `docs/architecture1.md`: Initial architecture document
+- `docs/todo.md`: Test-driven task breakdown
+- `src/`: Implementation following architecture
+- `tests/`: 388 tests with >90% coverage
+
+---
+
+*Document Version*: 1.0  
+*Date*: February 6, 2026  
+*Author*: Based on mcp-forge development experience