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ZoyaAfzal
ZoyaAfzalCOMMUNITY
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mcp-python-sdk

Build MCP servers with tools, resources, and prompts using the Python SDK.

Updated Jan 2, 2026
Search Tags:#python#ai#sdk#agents#mcp#server#tools
One-click install
npx skills add https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD --skill mcp-python-sdk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-python-sdk
Source: https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD/tree/main/.claude/skills/mcp-python-sdk
Command: npx skills add https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD --skill mcp-python-sdk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

MCP Python SDK standardizes building servers that expose tools, resources, and prompts for AI agents, enabling consistent interfaces and reusable patterns across projects.

Core Features & Use Cases

  • Rich decorators for tools, resources, prompts, and contexts with type-safe outputs
  • Structured outputs, lifespan context, and advanced transport integrations for scalable agents
  • Use cases include creating MCP servers for AI tool integrations, data pipelines, and agent-hosted services

Quick Start

Install the mcp package and expose a function as a tool with @mcp.tool() to enable AI agents to call it.

Frequently Asked Questions about mcp-python-sdk

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an MCP server in Python to expose tools for AI agents?▼

Build an MCP server in Python using the SDK's rich decorators like @mcp.tool() to expose functions, resources, and prompts for AI agents to call. It provides type-safe outputs and context management for standardized integrations.

What is the MCP Python SDK used for in agent workflows?▼

The MCP Python SDK standardizes building servers that expose tools, resources, and prompts for AI agents, enabling consistent interfaces and reusable patterns across Python services and data pipelines.

Can I use decorators to define resources and prompts for an MCP server?▼

Yes, you can use decorators to define tools, resources, and prompts for an MCP server. The Python SDK supports rich decorators with type-safe structured outputs and context management.

Does the Python MCP SDK support lifespan context and advanced transport integrations?▼

The Python MCP SDK supports lifespan context, advanced transport integrations, and optional low-level server APIs to enable scalable agent-hosted services and prompt-driven workflows.

How do I create structured outputs for AI tools in Python?▼

Create structured outputs for AI tools in Python by applying the SDK's rich decorators to your functions, ensuring type-safe responses that AI agents can reliably process during workflows.

When should I use a low-level server API instead of decorators for MCP tools?▼

Use low-level server APIs instead of decorators when building scalable agent-hosted services requiring advanced transport integrations and custom lifespan context management beyond standard prompt-driven workflows.

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