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Mcp sdk python bootstrapper

Skill a5c-ai/babysitter/library/specializations/cli-mcp-development/skills/mcp-sdk-python-bootstrapper

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Install
npx -y skills add a5c-ai/babysitter --skill mcp-sdk-python-bootstrapper

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What its author says it does

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Bootstrap MCP server with Python SDK, transport configuration, tool/resource handlers, and proper project structure.

SKILL.md

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MCP SDK Python Bootstrapper

Bootstrap a complete MCP server using the Python SDK with proper project structure.

Capabilities

  • Generate Python MCP server project structure
  • Create tool and resource handlers
  • Configure stdio/SSE transport layers
  • Set up proper typing with Pydantic
  • Implement error handling patterns
  • Configure Poetry/pip project

Usage

Invoke this skill when you need to:

  • Bootstrap a new MCP server in Python
  • Create tools and resources for AI consumption
  • Set up MCP transport layer
  • Implement MCP protocol handlers

Inputs

ParameterTypeRequiredDescription
projectNamestringYesName of the MCP server project
descriptionstringYesDescription of the server
toolsarrayNoList of tools to implement
resourcesarrayNoList of resources to expose
transportstringNoTransport type: stdio, sse (default: stdio)

Tool Structure

{
  "tools": [
    {
      "name": "search_files",
      "description": "Search for files matching a pattern",
      "parameters": {
        "pattern": { "type": "string", "description": "Search pattern" },
        "path": { "type": "string", "description": "Base path", "default": "." }
      }
    }
  ]
}

Output Structure

<projectName>/
├── pyproject.toml
├── README.md
├── .gitignore
├── src/
│   └── <package>/
│       ├── __init__.py
│       ├── __main__.py        # Entry point
│       ├── server.py          # MCP server setup
│       ├── tools/
│       │   ├── __init__.py
│       │   └── search.py      # Tool implementations
│       ├── resources/
│       │   ├── __init__.py
│       │   └── files.py       # Resource providers
│       └── types/
│           ├── __init__.py
│           └── schemas.py     # Pydantic models
└── tests/
    └── test_tools.py

Generated Code Patterns

Server Setup (src/<package>/server.py)

import asyncio
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, Resource

from .tools import register_tools
from .resources import register_resources

# Create server instance
server = Server("<projectName>")

# Register handlers
register_tools(server)
register_resources(server)

async def main():
    """Run the MCP server."""
    async with stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            server.create_initialization_options()
        )

def run():
    """Entry point for the server."""
    asyncio.run(main())

Tool Implementation (src/<package>/tools/search.py)

from typing import Any
from pydantic import BaseModel, Field
from mcp.server import Server
from mcp.types import Tool, TextContent

class SearchFilesInput(BaseModel):
    """Input schema for search_files tool."""
    pattern: str = Field(description="Search pattern (glob)")
    path: str = Field(default=".", description="Base path to search")

def register(server: Server) -> None:
    """Register the search_files tool."""

    @server.list_tools()
    async def list_tools() -> list[Tool]:
        return [
            Tool(
                name="search_files",
                description="Search for files matching a pattern",
                inputSchema=SearchFilesInput.model_json_schema()
            )
        ]

    @server.call_tool()
    async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
        if name != "search_files":
            raise ValueError(f"Unknown tool: {name}")

        # Validate input
        input_data = SearchFilesInput(**arguments)

        # Execute search
        from pathlib import Path
        matches = list(Path(input_data.path).glob(input_data.pattern))

        return [
            TextContent(
                type="text",
                text="\n".join(str(m) for m in matches)
            )
        ]

Resource Provider (src/<package>/resources/files.py)

from mcp.server import Server
from mcp.types import Resource, TextResourceContents

def register(server: Server) -> None:
    """Register file resources."""

    @server.list_resources()
    async def list_resources() -> list[Resource]:
        return [
            Resource(
                uri="file:///config",
                name="Configuration",
                description="Server configuration",
                mimeType="application/json"
            )
        ]

    @server.read_resource()
    async def read_resource(uri: str) -> TextResourceContents:
        if uri == "file:///config":
            return TextResourceContents(
                uri=uri,
                mimeType="application/json",
                text='{"version": "1.0.0"}'
            )
        raise ValueError(f"Unknown resource: {uri}")

Dependencies

[tool.poetry.dependencies]
python = ">=3.10"
mcp = "^1.0.0"
pydantic = "^2.0.0"

[tool.poetry.group.dev.dependencies]
pytest = "^8.0.0"
pytest-asyncio = "^0.23.0"

Workflow

  1. Create project structure - Set up Python package
  2. Generate server - MCP server with transport
  3. Create tools - Tool handlers with schemas
  4. Create resources - Resource providers
  5. Add types - Pydantic models
  6. Set up tests - Async test fixtures

Target Processes

  • mcp-server-bootstrap
  • mcp-tool-implementation
  • mcp-resource-provider

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