Kiro Powers is a curated collection of capabilities for building and composing MCP servers, React apps, and Raycast extensions. The kiro-powers MCP server leverages Python and the FastMCP framework to provide templates and tooling for creating MCP servers, including quickstart patterns, live documentation lookup, and tool design guidance. Use this server to scaffold MCP servers, explore curated templates, and wire up agent-friendly tools that can be invoked by AI agents. Typical workflows involve selecting a template (e.g., a stock-price watcher, weather watch, or MCP server builder), then configuring tools, transports, and endpoints to match your use case. The server is designed to help you go from concept to a functioning MCP service with minimal boilerplate, while preserving extensibility for advanced customizations.

Prerequisites:

• Python 3.13 or later
• Java runtime (for possible MCP dependencies) depending on your environment
• Git

Installation steps:

1. Clone the repository or install the power package: git clone https://github.com/praveenc/kiro-powers.git cd kiro-powers

2. (Optional) Create and activate a virtual environment: python -m venv venv

   Windows

   .\venv\Scripts\activate

   macOS/Linux

   source venv/bin/activate

3. Install dependencies (if this server is packaged as a Python module): pip install -r requirements.txt

4. Run the MCP server using the provided mcp_config configuration (adjust paths as needed): python -m praveenc.kiro_powers

5. Verify operation by querying available endpoints or using a local test client aligned with FastMCP tooling. If you cloned a different layout, follow the repository’s guidance for launching the MKIRO power server.

Tips and considerations:

• Ensure Python 3.13 is installed and available in your PATH.
• If you modify environment variables (e.g., POWER_CONFIG_PATH), point them to valid configuration files used by your MCP server.
• The server name kiro-powers is a logical grouping for MCP-related templates and tooling; actual templates may generate additional transport options (stdio, socket, etc.).
• When integrating with AI agents, expose clear tool definitions and input/output formats to minimize token usage and improve reliability.
• If you encounter transport or compatibility issues with FastMCP, verify that the Python bindings and any compiled dependencies are correctly installed for your platform.