# Project Summary: skill-to-mcp

## Overview

Successfully built a production-ready MCP server that converts AI Skills (Claude Skills format) into MCP server resources, allowing skills to be distributed separately from the package and edited by users.

## Key Features Implemented

### 1. Configurable Skills Directory ✅
- **CLI Option**: `--skills-dir` or `SKILLS_DIR` environment variable
- **Benefit**: Package can be installed separately from skills
- **Use Case**: Users can edit skills without modifying the package
- **Flexibility**: Support multiple skill collections for different projects

### 2. Core Functionality ✅

**Three MCP Tools:**
- `get_available_skills` - Lists all skills with metadata
- `get_skill_details` - Returns SKILL.md content + file listing
- `get_skill_related_file` - Reads specific files (with security)

**Skill Parser:**
- Parses YAML frontmatter from SKILL.md files
- Validates required fields (name, description)
- Recursively discovers skills
- Security: Directory traversal protection

### 3. Testing & Quality ✅

**Test Coverage:**
- 24 tests, all passing
- Unit tests for skill parser
- Integration tests for MCP tools
- Security tests (directory traversal)
- Configurable directory tests

**Code Quality:**
- All Ruff linting checks pass
- Type hints throughout
- Comprehensive docstrings (numpy style)
- Security best practices

### 4. Documentation ✅

**README.md:**
- Configuration examples (CLI & environment variables)
- Multiple installation methods
- MCP client configuration examples
- Docker deployment guide
- BioContextAI integration info
- Citation section

**Additional Docs:**
- CONTRIBUTING.md - Development guidelines
- EXAMPLE_USAGE.md - Practical examples
- Inline code documentation

### 5. Production Ready ✅

**Deployment Options:**
- Local development (uvx, pip)
- Production HTTP server
- Docker container
- Multiple transport options

**Configuration:**
- Environment variables
- Command-line options
- Validation and error handling
- Logging support

## Technical Architecture

```
skill-to-mcp/
├── src/skill_to_mcp/
│   ├── __init__.py          # Package exports
│   ├── main.py              # CLI with configurable skills dir
│   ├── mcp.py               # Dynamic MCP initialization
│   ├── skill_parser.py      # Core parsing logic
│   └── tools/
│       ├── __init__.py
│       └── _skills.py       # Dynamic tool registration
├── skills/                  # Example skills (single-cell-rna-qc)
├── tests/                   # Comprehensive test suite
└── docs/                    # Documentation
```

## Key Design Decisions

### 1. Dynamic Initialization
Instead of a global `mcp` instance, we use `initialize_mcp(skills_dir)` to create the server with a specific skills directory at runtime.

### 2. Tool Registration Pattern
Tools are registered dynamically using `register_skill_tools(mcp_server, skills_dir)`, creating a closure over the skill parser instance.

### 3. Separation of Concerns
- `skill_parser.py` - Pure parsing logic
- `tools/_skills.py` - MCP tool registration
- `main.py` - CLI and configuration
- `mcp.py` - Server initialization

### 4. Security First
- Path validation prevents directory traversal
- Skills directory must exist and be valid
- Required parameter validation

## Usage Examples

### Basic Usage
```bash
# Using uvx
SKILLS_DIR=/path/to/skills uvx skill_to_mcp

# Using command-line option
skill_to_mcp --skills-dir /path/to/skills
```

### MCP Client Configuration
```json
{
  "mcpServers": {
    "my-skills": {
      "command": "uvx",
      "args": ["skill_to_mcp", "--skills-dir", "/path/to/skills"],
      "env": {"UV_PYTHON": "3.12"}
    }
  }
}
```

### Docker Deployment
```bash
docker build -t skill-to-mcp .
docker run -p 8000:8000 -v /host/skills:/app/skills skill-to-mcp
```

## BioContextAI Integration

### Context
- Part of BioContextAI community initiative
- Registry at https://biocontext.ai/registry
- FAIR4RS compliant design
- Community-driven development

### Domain Flexibility
While developed for biomedical research:
- Works with any skill collection
- Domain-agnostic implementation
- Extensible architecture

## Version & Status

- **Version**: 0.1.0
- **Python**: 3.11+
- **Status**: Production Ready
- **License**: Apache 2.0
- **Tests**: 24/24 passing
- **Code Quality**: All checks passing

## Dependencies

**Core:**
- fastmcp - MCP server framework
- pyyaml - YAML parsing
- click - CLI framework

**Development:**
- pytest - Testing
- pytest-asyncio - Async testing
- ruff - Linting & formatting
- pre-commit - Git hooks

## Future Enhancements

Potential improvements:
1. Skill validation on server startup
2. Hot-reload when skills change
3. Skill versioning support
4. Skill dependencies/requirements
5. Skill search/filtering by tags
6. Usage metrics/analytics
7. Skill templates/scaffolding
8. Web UI for skill management

## Acknowledgments

- **BioContextAI** - Community and initiative
- **FastMCP** - Excellent framework
- **Claude** - Skills format specification
- **Anthropic** - Model Context Protocol

---

**Project Status**: ✅ Complete and Production Ready

The skill-to-mcp server successfully bridges Claude Skills and the Model Context Protocol, enabling flexible, user-editable skill collections that can be distributed and managed independently of the core package.
