engmanager-mcp
Provides LLMs with structured workflow guidance and next-step instructions for development procedures, ensuring consistent practices across projects.
README
Engineering Manager MCP
An MCP (Model Context Protocol) server that provides LLMs with structured workflow guidance and "next step" instructions for development procedures. Perfect for maintaining consistent development practices across projects without having to repeatedly explain procedures to AI assistants.
This project welcomes contributions from AI/LLM agents! Pull requests from Claude, GPT, and other AI models are actively encouraged.
Guidelines for AI contributors:
- Follow existing code patterns and documentation standards
- Include comprehensive commit messages explaining changes
- Test changes thoroughly before submitting PRs
- Update documentation when adding new features
🎯 Purpose
Engineering Manager MCP solves a common problem: LLMs forget your development procedures. Instead of repeatedly reminding your AI assistant about branch naming conventions, commit message formats, PR templates, or deployment steps, let Engineering Manager MCP provide that guidance on demand.
Use Cases:
- 🔄 Consistent Workflows - Maintain standardized development procedures across projects
- 📋 Step-by-Step Guidance - LLMs can query "what's next?" at any point in the workflow
- 🎯 Context-Aware Suggestions - Returns relevant workflow sections based on current task
- 🔧 Multi-Project Support - Different procedures for different projects
- 📝 Template Variables - Customize procedures with project-specific values
✨ Quick Start
Installation from PyPI
# Run directly with uvx (recommended)
uvx engmanager-mcp
# Or install with pip
pip install engmanager-mcp
Configure in Claude Code
For project-specific configuration, add to your local .mcp.json in the project directory, or add to your global claude_code_config.json:
{
"mcpServers": {
"engmanager": {
"command": "uvx",
"args": ["engmanager-mcp"],
"env": {
"ENGMANAGER_DEFAULT_PROJECT": "myproject"
}
}
}
}
Create Your First Project
- Create a project configuration:
mkdir -p ~/.config/engmanager-mcp
cat > ~/.config/engmanager-mcp/myproject-config.json <<EOF
{
"project_name": "myproject",
"procedure_file": "myproject-workflow.md",
"variables": {
"REPO_OWNER": "yourname",
"REPO_NAME": "myproject",
"DEFAULT_BRANCH": "main"
}
}
EOF
- Create a workflow procedure:
cat > ~/.config/engmanager-mcp/myproject-workflow.md <<EOF
# My Project Workflow
## 1. Branch Creation
Create a feature branch:
\`\`\`bash
git checkout -b feature/my-feature
\`\`\`
## 2. Development
Make your changes and commit:
\`\`\`bash
git add .
git commit -m "feat: description"
\`\`\`
## 3. Push & PR
Push and create a pull request:
\`\`\`bash
git push -u origin feature/my-feature
gh pr create --title "feat: Description"
\`\`\`
EOF
- Use in Claude:
You: "What's the first step in the workflow?"
Claude: [calls get_next_step tool]
Engineering Manager MCP: Returns "Step 1: Branch Creation..."
You: "What's next?"
Claude: [calls get_next_step with current_step=1]
Engineering Manager MCP: Returns "Step 2: Development..."
🔧 Available Tools
get_next_step
Get the next step in the workflow.
get_next_step(project="myproject", current_step=1)
Parameters:
project(optional): Project name (uses default if not specified)current_step(optional): Current step number (defaults to 0 for first step)
get_workflow_section
Get a specific section from the workflow by name.
get_workflow_section(section="Error Recovery Protocols", project="myproject")
list_workflow_steps
Get an overview of all numbered steps.
list_workflow_steps(project="myproject")
list_available_projects
List all configured projects.
list_available_projects()
get_project_info
Get detailed information about a project's configuration.
get_project_info(project="myproject")
📚 Available Resources
engmanager://procedures/{project}
Get the full procedure file with variables substituted.
engmanager://config/{project}
Get the project configuration.
engmanager://templates
Get documentation about template variables.
engmanager://projects
List all available projects with status.
🎨 Template Variables
Procedures support template variables for customization:
## Branch Creation for {PROJECT_NAME}
Create a branch:
\`\`\`bash
git checkout -b feature/my-feature
git push -u origin feature/my-feature
\`\`\`
Your repository: {REPO_OWNER}/{REPO_NAME}
Default branch: {DEFAULT_BRANCH}
Common Variables:
{PROJECT_NAME}- Project name{REPO_OWNER}- GitHub repository owner{REPO_NAME}- GitHub repository name{DEFAULT_BRANCH}- Default branch (main/master){ISSUE_NUMBER}- Current issue number{BRANCH_PREFIX}- Branch prefix (feature/fix/refactor)
Define custom variables in your project's config file.
📁 Project Structure
Important: The procedures/ directory is excluded from the PyPI package. When you install via uvx engmanager-mcp, you need to create your own workflow files in one of these locations:
~/.config/engmanager-mcp/ # Recommended for user-specific workflows
├── myproject-config.json # Project configuration
├── myproject-workflow.md # Workflow procedure
├── another-project-config.json
└── another-project-workflow.md
Configuration file locations (searched in order):
./procedures/(relative to current directory - for local development)~/.config/engmanager-mcp/(user config - recommended)/etc/engmanager-mcp/(system-wide)
Note: Example files (example-config.json, example-workflow.md) are only available in the GitHub repository for reference, not in the PyPI package.
⚙️ Configuration
Environment Variables
ENGMANAGER_DEFAULT_PROJECT- Default project nameENGMANAGER_MCP_LOG_LEVEL- Logging level (DEBUG, INFO, WARNING, ERROR)ENGMANAGER_PROCEDURES_DIR- Custom procedures directoryENGMANAGER_MCP_TRANSPORT- Transport mode (stdio or http)
Project Configuration Format
{
"project_name": "myproject",
"procedure_file": "myproject-workflow.md",
"variables": {
"REPO_OWNER": "username",
"REPO_NAME": "repository",
"DEFAULT_BRANCH": "main",
"CUSTOM_VAR": "custom_value"
}
}
Required Fields:
project_name- Unique project identifierprocedure_file- Filename of the markdown procedure
Optional Fields:
variables- Dictionary of template variables
📖 Example Use Cases
Use Case 1: Consistent Git Workflow
Problem: Different team members follow different git workflows.
Solution: Create a standardized procedure that LLMs can reference:
## 1. Branch Creation
- Always create from main
- Use conventional naming: feature/fix/refactor
- Format: <type>/<description>-issue-<number>
## 2. Commit Messages
- Use conventional commits format
- Include issue reference
- Add "Resolves #N" for auto-close
LLMs can now query these steps and follow them consistently.
Use Case 2: Multi-Project Development
Problem: Working on multiple projects with different procedures.
Solution: Configure multiple projects:
~/.config/engmanager-mcp/
├── frontend-config.json
├── frontend-workflow.md
├── backend-config.json
└── backend-workflow.md
LLMs can switch between project contexts easily.
Use Case 3: Onboarding Documentation
Problem: New team members (including AI assistants) need workflow guidance.
Solution: Comprehensive procedure files serve as executable documentation:
## Developer Setup
1. Clone repository
2. Install dependencies
3. Configure environment
4. Run development server
## Development Workflow
[Steps...]
## Deployment Process
[Steps...]
## Error Recovery
[Procedures...]
🔨 Development Setup
Local Installation
# Clone repository
git clone https://github.com/scarr7981/engmanager-mcp.git
cd engmanager-mcp
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate # Linux/Mac
# or
.venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Install in editable mode for development
pip install -e .
# Run locally
python -m engmanager_mcp.server
# or
engmanager-mcp
Development Mode with Claude Code
For local development and testing with Claude Code, add this to your project's .mcp.json file:
{
"mcpServers": {
"engmanager": {
"command": "/absolute/path/to/engmanager-mcp/.venv/bin/python",
"args": ["-m", "engmanager_mcp.server"],
"cwd": "/absolute/path/to/engmanager-mcp",
"env": {
"ENGMANAGER_DEFAULT_PROJECT": "example",
"ENGMANAGER_MCP_LOG_LEVEL": "DEBUG"
}
}
}
}
Important:
- Replace
/absolute/path/to/engmanager-mcpwith the actual path to your cloned repository - For Windows WSL, use:
/mnt/c/Users/username/path/to/engmanager-mcp/.venv/bin/python - For Windows native, use:
C:\\absolute\\path\\to\\engmanager-mcp\\.venv\\Scripts\\python.exe - The
.mcp.jsonfile should be in the project root directory - This file is gitignored, so your local config won't be committed
This configuration:
- Uses the virtual environment's Python interpreter directly
- Runs from your local development directory
- Enables DEBUG logging for troubleshooting
- Sets the example project as default
- Allows you to edit code and see changes immediately (restart Claude Code to reload)
Testing
Create a test project:
mkdir -p procedures
cat > procedures/test-config.json <<EOF
{
"project_name": "test",
"procedure_file": "example-workflow.md",
"variables": {
"REPO_OWNER": "testuser",
"REPO_NAME": "testrepo",
"DEFAULT_BRANCH": "main"
}
}
EOF
# Copy example workflow
cp procedures/example-workflow.md procedures/test-workflow.md
# Test with MCP inspector or Claude Code
📦 Publishing to PyPI
Build Package
# Install build tools
pip install build twine
# Build package
python -m build
# Check distribution
twine check dist/*
Upload to PyPI
# Upload to Test PyPI first
twine upload --repository testpypi dist/*
# Test installation
pip install --index-url https://test.pypi.org/simple/ engmanager-mcp
# Upload to PyPI
twine upload dist/*
Using GitHub Actions
This project can be configured with GitHub Actions for automatic PyPI publishing on tagged releases. See cargoshipper-mcp for reference.
🤝 Contributing
This project is inspired by the EXAMPLE_PROCEDURE.md workflow for Trowel.io and follows similar patterns.
Contribution Guidelines:
- Follow existing code patterns
- Add comprehensive docstrings
- Update README for new features
- Test with real procedures
- Include example configurations
📄 License
MIT License - See LICENSE file for details
🔗 Related Projects
- CargoShipper MCP - Infrastructure automation MCP server
- Model Context Protocol - Official MCP documentation
- FastMCP - Python MCP framework
💡 Tips & Best Practices
Writing Effective Procedures
- Use numbered steps for sequential workflows
- Include code examples in bash blocks
- Document error recovery procedures
- Add quality gates at key checkpoints
- Use template variables for project-specific values
Organizing Projects
- One config per project - Keep projects isolated
- Shared procedures - Reference common workflows
- Default project - Set for most common use case
- Version control - Keep procedures in git
LLM Integration
- Ask "what's next?" - Simple queries work best
- Provide context - Mention current step if known
- Query sections - Jump to specific workflow parts
- List steps - Get overview before starting
🐛 Troubleshooting
"Project not found"
- Check config file location
- Verify filename format:
<project>-config.json - Check
ENGMANAGER_DEFAULT_PROJECTenvironment variable
"Procedure file not found"
- Verify
procedure_filepath in config - Check procedures directory
- Ensure markdown file exists
"Missing template variables"
- Check procedure file for
{VARIABLE}syntax - Add missing variables to config
variablesobject - Variables must be UPPERCASE with underscores
"No projects configured"
- Add at least one
<project>-config.jsonfile - Check config search paths
- Verify JSON syntax in config files
📞 Support
- Issues: GitHub Issues
- Documentation: GitHub README
- MCP Help: Model Context Protocol Docs
Made with ❤️ for better AI-assisted development workflows
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。