sparesparrow mcp project orchestrator

sparesparrow mcp project orchestrator avatar

by sparesparrow

Analyze user input to identify suitable design patterns and project templates. Orchestrate the project, creating initial files from templates.

What is sparesparrow mcp project orchestrator

MCP Project Orchestrator

*CI/CD* *codecov* *PyPI version* *Python 3.9+* *License: MIT*

A comprehensive project orchestration tool for managing Model Context Protocol (MCP) projects, templates, prompts, and Mermaid diagrams.

Features

  • Template Management

    • Project templates for quick project setup
    • Component templates for modular development
    • Variable substitution and validation
    • Template discovery and versioning
  • Prompt Management

    • System and user prompt templates
    • Variable substitution
    • Prompt categorization and versioning
    • Easy prompt discovery and reuse
  • Mermaid Diagram Generation

    • Flowchart generation
    • Sequence diagram generation
    • Class diagram generation
    • SVG and PNG rendering
    • Diagram validation

Installation

pip install mcp-project-orchestrator

Or with Poetry:

poetry add mcp-project-orchestrator

Quick Start

Project Templates

from mcp_project_orchestrator.templates import TemplateManager

# Initialize template manager
manager = TemplateManager("path/to/templates")

# List available templates
templates = manager.list_templates()
print(templates)

# Apply a project template
manager.apply_template("fastapi-project", {
    "project_name": "my-api",
    "project_description": "My FastAPI project",
    "author_name": "John Doe",
    "author_email": "[email protected]"
})

Prompt Management

from mcp_project_orchestrator.prompts import PromptManager

# Initialize prompt manager
manager = PromptManager("path/to/prompts")

# List available prompts
prompts = manager.list_prompts()
print(prompts)

# Render a prompt with variables
rendered = manager.render_prompt("system-prompt", {
    "name": "User",
    "project": "MCP"
})
print(rendered)

Mermaid Diagrams

from mcp_project_orchestrator.mermaid import MermaidGenerator, MermaidRenderer

# Initialize generators
generator = MermaidGenerator()
renderer = MermaidRenderer()

# Generate a flowchart
flowchart = generator.generate_flowchart(
    nodes=[
        ("A", "Start"),
        ("B", "Process"),
        ("C", "End")
    ],
    edges=[
        ("A", "B", ""),
        ("B", "C", "")
    ]
)

# Render to SVG
renderer.render(flowchart, "flowchart.svg")

Project Structure

mcp-project-orchestrator/
โ”œโ”€โ”€ src/
โ”‚   โ””โ”€โ”€ mcp_project_orchestrator/
โ”‚       โ”œโ”€โ”€ templates/
โ”‚       โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚       โ”‚   โ”œโ”€โ”€ base.py
โ”‚       โ”‚   โ”œโ”€โ”€ project.py
โ”‚       โ”‚   โ”œโ”€โ”€ component.py
โ”‚       โ”‚   โ””โ”€โ”€ manager.py
โ”‚       โ”œโ”€โ”€ prompts/
โ”‚       โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚       โ”‚   โ”œโ”€โ”€ template.py
โ”‚       โ”‚   โ””โ”€โ”€ manager.py
โ”‚       โ””โ”€โ”€ mermaid/
โ”‚           โ”œโ”€โ”€ __init__.py
โ”‚           โ”œโ”€โ”€ generator.py
โ”‚           โ””โ”€โ”€ renderer.py
โ”œโ”€โ”€ tests/
โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”œโ”€โ”€ conftest.py
โ”‚   โ”œโ”€โ”€ test_templates.py
โ”‚   โ”œโ”€โ”€ test_prompts.py
โ”‚   โ””โ”€โ”€ test_mermaid.py
โ”œโ”€โ”€ docs/
โ”œโ”€โ”€ examples/
โ”œโ”€โ”€ .github/
โ”‚   โ””โ”€โ”€ workflows/
โ”‚       โ””โ”€โ”€ ci.yml
โ”œโ”€โ”€ pyproject.toml
โ”œโ”€โ”€ Containerfile
โ””โ”€โ”€ README.md

Development

  1. Clone the repository:
git clone https://github.com/yourusername/mcp-project-orchestrator.git
cd mcp-project-orchestrator
  1. Install dependencies:
poetry install
  1. Run tests:
poetry run pytest
  1. Run linting:
poetry run ruff check .
poetry run mypy src/mcp_project_orchestrator

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Model Context Protocol - The foundation for this project
  • Mermaid - For diagram generation
  • Poetry - For dependency management
  • Ruff - For linting
  • mypy - For type checking

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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